Description of the procedures and analysis present in Manuscript 1, Independent morphological correlates to aging, Mild Cognitive Impairment, and Alzheimer’s Disease, at the Doctorate Thesis presented to the Programa de Pós-Graduação em Ciência Médicas at the Instituto D’Or de Pesquisa e Ensino as a partial requirement to obtain the Doctorate Degree.

Part of the data used here cannot be shared due to restrictions of the Ethic Committee. Data can be shared upon reasonable request to the corresponding author. To fulfill these limitation, we will generate random data to simulate the results.

Get in touch with us () in case any help is needed, our aim is to improve the code as needed!

setwd("~/GitHub/FHPdeMoraes_thesis/Manuscript1_CorticalFolding_aging_MCI_AD")
## define functions

# test angular coeficinet versus theoretical value
test_coef <- function(reg, coefnum, val){
  co <- coef(summary(reg))
  tstat <- (co[coefnum,1] - val)/co[coefnum,2]
  2 * pt(abs(tstat), reg$df.residual, lower.tail = FALSE)
}

# wrap text
wrapper <- function(x, ...) paste(strwrap(x, ...), collapse = "\n")
library(readr)
library(tidyverse)
library(lubridate)
library(ggpubr)
library(kableExtra)
library(broom)
library(MASS)
library(cutpointr)
library(ggstatsplot)
library(effects)

1 set seed for random process

set.seed(1)
dados_raw <- read_csv("dados_raw.csv")
# estimate cortical folding variables
dados_raw <- dados_raw %>%
  mutate(
    # create new variables
    logAvgThickness = log10(AvgThickness),
    logTotalArea = log10(TotalArea),
    logExposedArea = log10(ExposedArea),
    localGI = TotalArea / ExposedArea,
    k = sqrt(AvgThickness) * TotalArea / (ExposedArea ^ 1.25),
    K = 1 / 4 * log10(AvgThickness ^ 2)  + log10(TotalArea) - 5 / 4 * log10(ExposedArea),
    S = 3 / 2 * log10(TotalArea) + 3 / 4 * log10(ExposedArea) - 9 /  4 * log10(AvgThickness ^
                                                                                 2) ,
    I = log10(TotalArea) + log10(ExposedArea) + log10(AvgThickness ^ 2),
    c = as.double(ifelse(
      ROI == "hemisphere", NA, 4 * pi / GaussianCurvature
    )),
    TotalArea_corrected = ifelse(ROI == "hemisphere", TotalArea, TotalArea * c),
    ExposedArea_corrected = ifelse(ROI == "hemisphere", ExposedArea, ExposedArea * c),
    logTotalArea_corrected = log10(TotalArea_corrected),
    logExposedArea_corrected = log10(ExposedArea_corrected),
    localGI_corrected = ifelse(
      ROI == "hemisphere",
      TotalArea / ExposedArea,
      TotalArea_corrected / ExposedArea_corrected
    ),
    k_corrected = ifelse(
      ROI == "hemisphere",
      sqrt(AvgThickness) * log10(TotalArea) / (log10(ExposedArea) ^ 1.25),
      sqrt(AvgThickness) * log10(TotalArea_corrected) / (log10(ExposedArea_corrected ^
                                                                 1.25))
    ),
    K_corrected =  ifelse(
      ROI == "hemisphere",
      1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea) - 5 / 4 * log10(ExposedArea),
      1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea_corrected) - 5 / 4 * log10(ExposedArea_corrected)
    ),
    I_corrected = ifelse(
      ROI == "hemisphere",
      log10(TotalArea) + log10(ExposedArea) + log10(AvgThickness ^ 2) ,
      log10(TotalArea_corrected) + log10(ExposedArea_corrected) + log10(AvgThickness ^ 2)
    ),
    S_corrected = ifelse(
      ROI == "hemisphere",
      3 / 2 * log10(TotalArea) + 3 / 4 * log10(ExposedArea) - 9 /  4 * log10(AvgThickness ^ 2) ,
      3 / 2 * log10(TotalArea_corrected) + 3 / 4 * log10(ExposedArea_corrected) - 9 /  4 * log10(AvgThickness ^ 2)
    ),
    Knorm = K_corrected / sqrt(1 + (1 / 4) ^ 2 + (5 / 4) ^ 2),
    Snorm = S_corrected / sqrt((3 / 2) ^ 2 + (3 / 4) ^ 2 + (9 / 4) ^ 2),
    Inorm = I_corrected / sqrt(1 ^ 2 + 1 ^ 2 + 1 ^ 1)
  )

# create age intervals
dados_raw$Age_interval <- cut(dados_raw$Age,
                                       breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100),
                                       right = FALSE,
                                       include.lowest = TRUE)

dados_raw$Age_interval10 <- cut(dados_raw$Age,
                                         breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
                                         right = FALSE,
                                         include.lowest = TRUE)
dados_all <- dados_raw %>% filter(
    Diagnostic == "CONTROLE" |
      Diagnostic == "CCL" |
      Diagnostic == "ALZ", !is.na(logAvgThickness), ExposedArea != 0 | !is.na(localGI), !is.infinite(logExposedArea)) %>% 
  droplevels()

dados <- dados_all
# rename diagnostics
dados$Diagnostic[dados$Diagnostic == "CONTROLE"] <- "CTL"
dados$Diagnostic[dados$Diagnostic == "ALZ"] <- "AD"
dados$Diagnostic[dados$Diagnostic == "CCL"] <- "MCI"
dados$Diagnostic <- factor(dados$Diagnostic, levels = c("AD", "MCI","CTL"))

# filter data
dados <- dados %>%
  filter(machine == "Philips-Achieva", # include only subjects acquired at Philips Achieva 3T
                          ESC == 8 | ESC > 8, # include only subjects with 8 years of scholarship or more
                          Session == 1) %>% # use only data from Session 1
  droplevels() # delete factor levels

2 Deaging

# define age for deaging
Age.cor = 25

## Avg thickness ----
decay_AvgThickness <-
  filter(
    dados,
    Diagnostic == "CTL",!is.na(TotalArea),!is.nan(TotalArea),!is.infinite(TotalArea)
  ) %>%
  droplevels() %>%
  group_by(ROI) %>%
  do(fit_decay_AvgThickness = tidy(rlm(AvgThickness ~ Age, data = .), conf.int =
                                     TRUE)) %>%
  unnest(cols = c(fit_decay_AvgThickness)) %>%
  filter(term == "Age") %>%
  mutate(c_AvgThickness = estimate,
         std_error_c_AvgThickness = std.error) %>%
  dplyr::select(c(ROI, c_AvgThickness, std_error_c_AvgThickness))

## TotalArea ----
decay_TotalArea <-
  filter(
    dados,
    Diagnostic == "CTL",
    !is.na(TotalArea),
    !is.nan(TotalArea),
    !is.infinite(TotalArea)
  ) %>%
  droplevels() %>%
  group_by(ROI) %>%
  do(fit_decay_TotalArea = tidy(rlm(TotalArea ~ Age, data = .), conf.int =
                                  TRUE)) %>%
  unnest(cols = c(fit_decay_TotalArea)) %>%
  filter(term == "Age") %>%
  mutate(c_TotalArea = estimate,
         std_error_c_TotalArea = std.error) %>%
  dplyr::select(c(ROI, c_TotalArea, std_error_c_TotalArea))

## ExposedArea ----
decay_ExposedArea <-
  filter(
    dados,
    Diagnostic == "CTL",
    !is.na(ExposedArea),
    !is.nan(ExposedArea),
    !is.infinite(ExposedArea)
  ) %>%
  droplevels() %>%
  group_by(ROI) %>%
  do(fit_decay_ExposedArea = tidy(rlm(ExposedArea ~ Age, data = .), conf.int = TRUE)) %>%
  unnest(cols = c(fit_decay_ExposedArea)) %>%
  filter(term == "Age") %>%
  mutate(c_ExposedArea = estimate,
         std_error_c_ExposedArea = std.error) %>%
  dplyr::select(c(ROI, c_ExposedArea, std_error_c_ExposedArea))

## join
dados <- full_join(dados, decay_AvgThickness) %>%
  full_join(decay_TotalArea) %>%
  full_join(decay_ExposedArea) %>%
  mutate(
    AvgThickness_age_decay = AvgThickness - c_AvgThickness * (Age - Age.cor),
    logAvgThickness_age_decay = log10(AvgThickness_age_decay),
    TotalArea_age_decay = TotalArea - c_TotalArea * (Age - Age.cor),
    logTotalArea_age_decay = log10(TotalArea_age_decay),
    ExposedArea_age_decay = ExposedArea - c_ExposedArea * (Age - Age.cor),
    logExposedArea_age_decay = log10(ExposedArea_age_decay),
    K_age_decay = log10(TotalArea_age_decay) + 1/4*log10(AvgThickness_age_decay^2) - 5/4*log10(ExposedArea_age_decay),
    I_age_decay = log10(TotalArea_age_decay) + log10(ExposedArea_age_decay) + log10(AvgThickness_age_decay^2),
    S_age_decay = 3/2*log10(TotalArea_age_decay) + 3/4*log10(ExposedArea_age_decay) - 9/4*log10(AvgThickness_age_decay^2))

dados$logAvgThickness <- as.double(dados$logAvgThickness)
dados$logExposedArea <- as.double(dados$logExposedArea)
dados$logTotalArea   <- as.double(dados$logTotalArea)
dados_v1 <- filter(dados, ROI == "F" | ROI == "T" | ROI == "O" | ROI == "P" | ROI == "hemisphere") %>%
  droplevels()

# lobe data
dados_lobos_v1 <- unique(filter(dados, ROI == "F" | ROI == "T" | ROI == "O" | ROI == "P",  !is.na(K_age_decay), SUBJ != "SUBJ211", SUBJ != "SUBJ223")) %>%
  droplevels()

# hemisphere data
dados_hemi_v1 <- unique(filter(dados, ROI == "hemisphere", !is.na(K_age_decay)))

3 Data description

Diagnostic N age age_range ESC T AT AE k K S I
AD 13 77 ± 6.1 63 ; 86 13 ± 3 2.4 ± 0.079 95000 ± 9300 37000 ± 3000 0.28 ± 0.01 -0.55 ± 0.015 9.2 ± 0.13 10 ± 0.069
MCI 33 72 ± 4.6 62 ; 82 13 ± 2.4 2.5 ± 0.085 97000 ± 8500 37000 ± 2800 0.29 ± 0.0096 -0.53 ± 0.014 9.2 ± 0.12 10 ± 0.063
CTL 77 66 ± 8.4 43 ; 80 15 ± 2.2 2.5 ± 0.099 98000 ± 7800 37000 ± 2400 0.3 ± 0.0095 -0.52 ± 0.014 9.1 ± 0.1 10 ± 0.072

4 Results

4.1 Cortical Folding Model

summary(lm(
  1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
  data = dados_hemi_v1,
  na.action = na.omit
))
## 
## Call:
## lm(formula = 1/2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea), 
##     data = dados_hemi_v1, na.action = na.omit)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.044853 -0.009505  0.000683  0.010895  0.035787 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         0.01789    0.14975   0.119    0.905    
## log10(ExposedArea)  1.13042    0.03275  34.511   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01519 on 244 degrees of freedom
## Multiple R-squared:   0.83,  Adjusted R-squared:  0.8293 
## F-statistic:  1191 on 1 and 244 DF,  p-value: < 2.2e-16
tidy(lm(
  1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
  data = dados_hemi_v1,
  na.action = na.omit
), conf.int = TRUE)
## # A tibble: 2 × 7
##   term               estimate std.error statistic  p.value conf.low conf.high
##   <chr>                 <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
## 1 (Intercept)          0.0179    0.150      0.119 9.05e- 1   -0.277     0.313
## 2 log10(ExposedArea)   1.13      0.0328    34.5   7.46e-96    1.07      1.19
paste(
  "Student's t test comapring slope with theoretical value 1.25. t = ",
  signif(abs(coef(summary(
    lm(
      1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
      data = dados_hemi_v1,
      na.action = na.omit
    )
  ))[2, 1] - 1.25) / coef(summary(
    lm(
      1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
      data = dados_hemi_v1,
      na.action = na.omit
    )
  ))[2, 2], 3) 
)
## [1] "Student's t test comapring slope with theoretical value 1.25. t =  3.65"
paste(
  "Student's t test comapring slope with theoretical value 1.25. p value = ",
  signif(test_coef(
    lm(
      1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
      data = dados_hemi_v1,
      na.action = na.omit
    ),
    2,
    1.25
  ),3)
)
## [1] "Student's t test comapring slope with theoretical value 1.25. p value =  0.00032"

5 Figure 1 A

fig2a <- ggplot(dados_hemi_v1,
                aes(
                  log10(ExposedArea),
                  log10(sqrt(AvgThickness) * TotalArea),
                  color = Diagnostic,
                  fill = Diagnostic,
                  alpha = 0.4
                )) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  geom_abline(slope = 1.25,
              intercept = coef(lm(
                log10(sqrt(AvgThickness) * TotalArea) ~ log10(ExposedArea),
                data = dados_hemi_v1,
                na.action = na.omit
              ))[1],
              color = "black") +
  labs(x = expression('log'[10] * 'A'['E']),
       y = expression('log'[10] * 'A'['T'] * sqrt('T'))) +
  guides(alpha = "none") +
  theme_pubr() +
  scale_x_continuous(limits = c(4.45, 4.65))
lm_fit_comp_idor <- dados_hemi_v1 %>%
  group_by(Diagnostic) %>%
  do(fit_comp_idor = glance(lm(log10(sqrt(AvgThickness)*TotalArea) ~ log10(ExposedArea), data = ., na.action = na.omit), conf.int = TRUE)) %>% unnest()

lm_fit_comp_idor %>%
  kable(digits = 2) %>%
  kable_styling()
Diagnostic r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
AD 0.87 0.86 0.01 158.83 0 1 73.85 -141.71 -137.93 0.01 24 26
MCI 0.88 0.88 0.01 487.51 0 1 199.00 -391.99 -385.42 0.01 64 66
CTL 0.85 0.85 0.01 871.32 0 1 442.46 -878.92 -869.81 0.03 152 154 
lm_fit_comp_idor <- dados_hemi_v1 %>%
  group_by(Diagnostic) %>%
  do(fit_comp_idor = tidy(lm(log10(sqrt(AvgThickness)*TotalArea) ~ log10(ExposedArea), data = ., na.action = na.omit), conf.int = TRUE)) %>% unnest()

lm_fit_comp_idor %>%
  kable(digits = 2) %>%
  kable_styling()
Diagnostic term estimate std.error statistic p.value conf.low conf.high
AD (Intercept) 0.20 0.39 0.50 0.62 -0.62 1.01
AD log10(ExposedArea) 1.09 0.09 12.60 0.00 0.91 1.27
MCI (Intercept) 0.53 0.21 2.50 0.02 0.11 0.95
MCI log10(ExposedArea) 1.02 0.05 22.08 0.00 0.93 1.11
CTL (Intercept) -0.23 0.18 -1.27 0.21 -0.60 0.13
CTL log10(ExposedArea) 1.19 0.04 29.52 0.00 1.11 1.27 
N_subj_diag <- dados_hemi_v1 %>%
  group_by(Diagnostic) %>%
  summarise(N_SUBJ = n_distinct(SUBJ))

ggplot(
  data = filter(lm_fit_comp_idor, term == "log10(ExposedArea)"),
  aes(x = Diagnostic,
      y = estimate, color = Diagnostic)
) +
  geom_point() +
  geom_errorbar(aes(ymin = estimate - std.error, ymax = estimate + std.error)) +
  geom_hline(yintercept = 1.25, linetype = "dashed") +
  theme_pubr() +
  labs(y = "Slope") +
  theme(legend.position = "none"
  )

5.1 Correlation within cortical fodling variables and age healthy subjects)

5.1.1 Slope alpha (SI Appendix, Text SI)

lm_Age <-
  filter(
    dados_hemi_v1,
    Diagnostic == "CTL",
    Age_interval != "[40,45)",
    Age_interval != "[80,85)"
  ) %>%
  group_by(Age_interval) %>%
  do(fit_Age = tidy(
    lm(
      1 / 2 * log10(AvgThickness) +  log10(TotalArea) ~  log10(ExposedArea),
      data = .,
      na.action = na.omit
    ),
    conf.int = TRUE
  )) %>%
  unnest(cols = c(fit_Age))

N_subj <- filter(dados_hemi_v1, Diagnostic == "CTL", Age_interval != "[40,45)", Age_interval != "[80,85)") %>%
  group_by(Age_interval) %>%
  summarise(N_SUBJ = n_distinct(SUBJ))
                                           
fig1a <- ggplot(
  data = filter(lm_Age, term == "log10(ExposedArea)"),
  aes(
  x = Age_interval,
  y = estimate
  )
  ) +
  geom_point() +
  geom_errorbar(aes(ymin = estimate - std.error, ymax = estimate + std.error)) +
  geom_smooth(method = "lm", se = TRUE) +
  geom_hline(yintercept = 1.25, linetype = "dashed") +
  geom_text(aes(label = N_subj$N_SUBJ), nudge_y = 0.6) +
  theme_pubr() +
  labs(y = "Slope", x = "Age [years]") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
lm_Age <- lm_Age %>% mutate(Age_interval = as.double((str_sub(Age_interval,2,3))))

cor.test(filter(lm_Age, term == "log10(ExposedArea)")$estimate, filter(lm_Age, term == "log10(ExposedArea)")$Age_interval)
## 
##  Pearson's product-moment correlation
## 
## data:  filter(lm_Age, term == "log10(ExposedArea)")$estimate and filter(lm_Age, term == "log10(ExposedArea)")$Age_interval
## t = -2.8822, df = 5, p-value = 0.0345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.96750265 -0.09146064
## sample estimates:
##        cor 
## -0.7901004

5.1.2 K (Figure 1 B)

fig1b <- ggplot(data = dados_hemi_v1, aes(Age, K, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  theme_pubr() +
    guides(alpha = "none", color = "none", fill = "none") + 
  labs(x = "Age [years]")

fig1b

cor.test(filter(dados_hemi_v1, Diagnostic == "CTL")$K, filter(dados_hemi_v1, Diagnostic == "CTL")$Age)
## 
##  Pearson's product-moment correlation
## 
## data:  filter(dados_hemi_v1, Diagnostic == "CTL")$K and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = -4.176, df = 152, p-value = 4.981e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4558437 -0.1713459
## sample estimates:
##        cor 
## -0.3208125

5.1.3 S

ggplot(data = dados_hemi_v1, aes(Age, S, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  theme_pubr() +
    guides(alpha = "none", color = "none", fill = "none") + 
  labs(x = "Age [years]")

cor.test(filter(dados_hemi_v1, Diagnostic == "CTL")$S, filter(dados_hemi_v1, Diagnostic == "CTL")$Age)
## 
##  Pearson's product-moment correlation
## 
## data:  filter(dados_hemi_v1, Diagnostic == "CTL")$S and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = 1.546, df = 152, p-value = 0.1242
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03441253  0.27713228
## sample estimates:
##       cor 
## 0.1244254

5.1.4 I

ggplot(data = dados_hemi_v1, aes(Age, I, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  theme_pubr() +
    guides(alpha = "none", color = "none", fill = "none") + 
  labs(x = "Age [years]")

cor.test(filter(dados_hemi_v1, Diagnostic == "CTL")$I, filter(dados_hemi_v1, Diagnostic == "CTL")$Age)
## 
##  Pearson's product-moment correlation
## 
## data:  filter(dados_hemi_v1, Diagnostic == "CTL")$I and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = -6.6178, df = 152, p-value = 5.879e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5871863 -0.3402379
## sample estimates:
##        cor 
## -0.4729482

6 Figure 1

fig1_alt_2 <-
  ggarrange(
    fig2a,
    fig1b,
    labels = c("A", "B"),
    ncol = 1,
    common.legend = TRUE,
    legend = "bottom"
  )
fig1_alt_2
\label{fig:figure1}Age and diagnostic effects in cortical gyrification. We included 77 CTL (blue), 33 MCI (green) and 13 AD (red) subjects (A) Linear fitting for the model variables in each Diagnostic group, CTL (adjusted R²=0.85, and CTL (adjusted R²=0.097, MCI (adjusted R²=0.044, p=0.0051), p<0.0001), and AD (adjusted R²=0.86, p<0.0001), MCI (adjusted R²=0.88, p<0.0001). As the severity of the disease increase, the linear tendency is downshifted, with smaller linear intercepts (K). (B) K linear tendency with age with its 95% CI for the three diagnostics groups: AD (adjusted R²=0.026

Age and diagnostic effects in cortical gyrification. We included 77 CTL (blue), 33 MCI (green) and 13 AD (red) subjects (A) Linear fitting for the model variables in each Diagnostic group, CTL (adjusted R²=0.85, and CTL (adjusted R²=0.097, MCI (adjusted R²=0.044, p=0.0051), p<0.0001), and AD (adjusted R²=0.86, p<0.0001), MCI (adjusted R²=0.88, p<0.0001). As the severity of the disease increase, the linear tendency is downshifted, with smaller linear intercepts (K). (B) K linear tendency with age with its 95% CI for the three diagnostics groups: AD (adjusted R²=0.026

6.1 Diagnostic discrimination and prediction.

6.1.1 K difference

aov <- aov(K ~ Diagnostic, data = dados_hemi_v1)
summary(aov)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.01121 0.005607   28.27 9.13e-12 ***
## Residuals   243 0.04819 0.000198                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = dados_hemi_v1)
## 
## $Diagnostic
##                diff         lwr        upr     p adj
## MCI-AD  0.015650636 0.007961177 0.02334010 0.0000083
## CTL-AD  0.021969964 0.014928714 0.02901121 0.0000000
## CTL-MCI 0.006319328 0.001433485 0.01120517 0.0071486

6.1.2 K is reduced with age, as cortical thickness, total area and exposed area

K decrease with age is shown on Figure 1 B. Cortical Thickness, Total area and Exposed area:

T <- ggplot(data = dados_hemi_v1, aes(Age, AvgThickness, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  theme_pubr() +
    guides(alpha = "none", color = "none", fill = "none") + 
  labs(x = "Age [years]")

AT <- ggplot(data = dados_hemi_v1, aes(Age, TotalArea, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  theme_pubr() +
    guides(alpha = "none", color = "none", fill = "none") + 
  labs(x = "Age [years]", y = "Total Area 10^-5 ") +
  scale_y_continuous(
    labels = function(x)
      x / 10000)

AE <- ggplot(data = dados_hemi_v1, aes(Age, ExposedArea, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  theme_pubr() +
    guides(alpha = "none", color = "none", fill = "none") + 
  labs(x = "Age [years]", y = "Exposed Area 10^-5 ") +
  scale_y_continuous(
    labels = function(x)
      x / 10000)


ggarrange(T, AT, AE, ncol = 1, common.legend = TRUE, legend = "bottom")

6.1.3 Lobes diagnostic discrimination

6.1.3.1 Frontal Lobe

aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(aov)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.00904 0.004519   14.71 9.49e-07 ***
## Residuals   239 0.07345 0.000307                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
## 
## $Diagnostic
##                diff           lwr        upr     p adj
## MCI-AD  0.014609191  0.0050152217 0.02420316 0.0011588
## CTL-AD  0.019888810  0.0111100645 0.02866756 0.0000006
## CTL-MCI 0.005279619 -0.0008538009 0.01141304 0.1072424

6.1.3.2 Occipital Lobe

aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(aov)
##              Df  Sum Sq  Mean Sq F value  Pr(>F)    
## Diagnostic    2 0.01088 0.005439   10.07 6.3e-05 ***
## Residuals   239 0.12902 0.000540                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
## 
## $Diagnostic
##                diff          lwr        upr     p adj
## MCI-AD  0.014822783  0.002107549 0.02753802 0.0176045
## CTL-AD  0.021529149  0.009894360 0.03316394 0.0000563
## CTL-MCI 0.006706366 -0.001422478 0.01483521 0.1282589

6.1.3.3 Parietal Lobe

aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(aov)
##              Df  Sum Sq  Mean Sq F value  Pr(>F)    
## Diagnostic    2 0.01254 0.006269   16.65 1.7e-07 ***
## Residuals   239 0.08998 0.000376                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
## 
## $Diagnostic
##                diff          lwr         upr     p adj
## MCI-AD  0.021557695  0.010938941 0.032176449 0.0000088
## CTL-AD  0.023693603  0.013977151 0.033410055 0.0000001
## CTL-MCI 0.002135908 -0.004652656 0.008924473 0.7387226

6.1.3.4 Temporal Lobe

aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(aov)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.01975 0.009876   28.49 7.97e-12 ***
## Residuals   239 0.08284 0.000347                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
## 
## $Diagnostic
##               diff         lwr        upr     p adj
## MCI-AD  0.01547690 0.005288318 0.02566549 0.0011957
## CTL-AD  0.02747185 0.018149018 0.03679469 0.0000000
## CTL-MCI 0.01199495 0.005481393 0.01850851 0.0000615

7 Figure S1

# K ----

dados_hemi_v1 %>%
  group_by(Diagnostic) %>%
  summarise(N_SUBJ = n_distinct(SUBJ))
## # A tibble: 3 × 2
##   Diagnostic N_SUBJ
##   <fct>       <int>
## 1 AD             13
## 2 MCI            33
## 3 CTL            77
dados_lobos_v1 %>%
  group_by(Diagnostic) %>%
  summarise(N_SUBJ = n_distinct(SUBJ))
## # A tibble: 3 × 2
##   Diagnostic N_SUBJ
##   <fct>       <int>
## 1 AD             13
## 2 MCI            33
## 3 CTL            77
a <- aov(K ~ Diagnostic, data = dados_hemi_v1)
summary(a)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.01121 0.005607   28.27 9.13e-12 ***
## Residuals   243 0.04819 0.000198                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = dados_hemi_v1)
## 
## $Diagnostic
##                diff         lwr        upr     p adj
## MCI-AD  0.015650636 0.007961177 0.02334010 0.0000083
## CTL-AD  0.021969964 0.014928714 0.02901121 0.0000000
## CTL-MCI 0.006319328 0.001433485 0.01120517 0.0071486
a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "K", agecorrection = "no")

a.F <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.00904 0.004519   14.71 9.49e-07 ***
## Residuals   239 0.07345 0.000307                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.F)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
## 
## $Diagnostic
##                diff           lwr        upr     p adj
## MCI-AD  0.014609191  0.0050152217 0.02420316 0.0011588
## CTL-AD  0.019888810  0.0111100645 0.02866756 0.0000006
## CTL-MCI 0.005279619 -0.0008538009 0.01141304 0.1072424
a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "K", agecorrection = "no")

a.O <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)
##              Df  Sum Sq  Mean Sq F value  Pr(>F)    
## Diagnostic    2 0.01088 0.005439   10.07 6.3e-05 ***
## Residuals   239 0.12902 0.000540                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.O)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
## 
## $Diagnostic
##                diff          lwr        upr     p adj
## MCI-AD  0.014822783  0.002107549 0.02753802 0.0176045
## CTL-AD  0.021529149  0.009894360 0.03316394 0.0000563
## CTL-MCI 0.006706366 -0.001422478 0.01483521 0.1282589
a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "K", agecorrection = "no")

a.P <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)
##              Df  Sum Sq  Mean Sq F value  Pr(>F)    
## Diagnostic    2 0.01254 0.006269   16.65 1.7e-07 ***
## Residuals   239 0.08998 0.000376                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.P)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
## 
## $Diagnostic
##                diff          lwr         upr     p adj
## MCI-AD  0.021557695  0.010938941 0.032176449 0.0000088
## CTL-AD  0.023693603  0.013977151 0.033410055 0.0000001
## CTL-MCI 0.002135908 -0.004652656 0.008924473 0.7387226
a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "K", agecorrection = "no")

a.T <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.01975 0.009876   28.49 7.97e-12 ***
## Residuals   239 0.08284 0.000347                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.T)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
## 
## $Diagnostic
##               diff         lwr        upr     p adj
## MCI-AD  0.01547690 0.005288318 0.02566549 0.0011957
## CTL-AD  0.02747185 0.018149018 0.03679469 0.0000000
## CTL-MCI 0.01199495 0.005481393 0.01850851 0.0000615
a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "K", agecorrection = "no")

aov_summary <- full_join(a.TukeyHSD, a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)

# K  age decay ----

a <- aov(K_age_decay ~ Diagnostic, data = dados_hemi_v1)
summary(a)
##              Df  Sum Sq   Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.00478 0.0023923   15.69 3.91e-07 ***
## Residuals   243 0.03706 0.0001525                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = dados_hemi_v1)
## 
## $Diagnostic
##                diff          lwr         upr     p adj
## MCI-AD  0.011591895  0.004849210 0.018334579 0.0001997
## CTL-AD  0.014605421  0.008431134 0.020779708 0.0000002
## CTL-MCI 0.003013526 -0.001270742 0.007297794 0.2233236
a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "K", agecorrection = "yes")

a.F <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)
##              Df  Sum Sq   Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.00361 0.0018049   7.345 0.000802 ***
## Residuals   239 0.05873 0.0002457                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.F)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
## 
## $Diagnostic
##                diff          lwr         upr     p adj
## MCI-AD  0.010592555  0.002013654 0.019171456 0.0109327
## CTL-AD  0.012750436  0.004900506 0.020600366 0.0004781
## CTL-MCI 0.002157881 -0.003326606 0.007642368 0.6231806
a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "K", agecorrection = "yes")

a.O <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)
##              Df  Sum Sq   Mean Sq F value  Pr(>F)   
## Diagnostic    2 0.00575 0.0028763   6.521 0.00175 **
## Residuals   239 0.10542 0.0004411                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.O)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
## 
## $Diagnostic
##                diff           lwr        upr     p adj
## MCI-AD  0.011558848  6.491843e-05 0.02305278 0.0483683
## CTL-AD  0.015846744  5.329482e-03 0.02636401 0.0013265
## CTL-MCI 0.004287895 -3.060169e-03 0.01163596 0.3549990
a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "K", agecorrection = "yes")

a.P <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.00604 0.003020   10.23 5.43e-05 ***
## Residuals   239 0.07051 0.000295                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.P)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
## 
## $Diagnostic
##                  diff          lwr         upr     p adj
## MCI-AD   0.0167424548  0.007342507 0.026142402 0.0001111
## CTL-AD   0.0157902985  0.007189088 0.024391509 0.0000651
## CTL-MCI -0.0009521562 -0.006961538 0.005057226 0.9259479
a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "K", agecorrection = "yes")

a.T <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.00681 0.003405   13.31 3.31e-06 ***
## Residuals   239 0.06114 0.000256                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a.T)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
## 
## $Diagnostic
##                diff          lwr        upr     p adj
## MCI-AD  0.009981324 0.0012282029 0.01873445 0.0208566
## CTL-AD  0.016475471 0.0084661238 0.02448482 0.0000066
## CTL-MCI 0.006494146 0.0008982799 0.01209001 0.0182332
a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "K", agecorrection = "yes")

aov_summary <- full_join(aov_summary, a.TukeyHSD) %>% full_join(a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)

# logAvgThickness ----

a <- aov(logAvgThickness ~ Diagnostic, data = dados_hemi_v1)
summary(a)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.01343 0.006717    25.3 1.05e-10 ***
## Residuals   243 0.06452 0.000265                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "log[10]T", agecorrection = "no")

a.F <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.01143 0.005714   16.86 1.41e-07 ***
## Residuals   239 0.08098 0.000339                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "log[10]T", agecorrection = "no")

a.O <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)
##              Df Sum Sq  Mean Sq F value  Pr(>F)   
## Diagnostic    2 0.0043 0.002149   5.142 0.00651 **
## Residuals   239 0.0999 0.000418                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "log[10]T", agecorrection = "no")

a.P <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)
##              Df  Sum Sq  Mean Sq F value  Pr(>F)    
## Diagnostic    2 0.00769 0.003843    11.8 1.3e-05 ***
## Residuals   239 0.07786 0.000326                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "log[10]T", agecorrection = "no")

a.T <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.03180 0.015898   41.54 3.28e-16 ***
## Residuals   239 0.09146 0.000383                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "log[10]T", agecorrection = "no")

aov_summary <- full_join(aov_summary, a.TukeyHSD) %>% full_join(a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)

# logAvgThickness  age decay ----

a <- aov(logAvgThickness_age_decay ~ Diagnostic, data = dados_hemi_v1)
summary(a)
##              Df  Sum Sq   Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.00333 0.0016661   9.025 0.000166 ***
## Residuals   243 0.04486 0.0001846                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "log[10]T", agecorrection = "yes")

a.F <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)
##              Df  Sum Sq   Mean Sq F value  Pr(>F)   
## Diagnostic    2 0.00259 0.0012967   5.359 0.00529 **
## Residuals   239 0.05782 0.0002419                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "log[10]T", agecorrection = "yes")

a.O <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)
##              Df  Sum Sq   Mean Sq F value Pr(>F)
## Diagnostic    2 0.00095 0.0004756   1.316   0.27
## Residuals   239 0.08639 0.0003615
a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "log[10]T", agecorrection = "yes")

a.P <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)
##              Df  Sum Sq   Mean Sq F value Pr(>F)
## Diagnostic    2 0.00089 0.0004448   2.067  0.129
## Residuals   239 0.05144 0.0002152
a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "log[10]T", agecorrection = "yes")

a.T <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)
##              Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic    2 0.01274 0.006371   22.82 8.51e-10 ***
## Residuals   239 0.06672 0.000279                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "log[10]T", agecorrection = "yes")

# ----
aov_summary <- full_join(aov_summary, a.TukeyHSD) %>% full_join(a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)

agecorrection <- c(
  "no" = "Raw data",
  "yes" = "After age correction"
)

figs1 <- ggplot(data = filter(aov_summary, `p adj` < 0.05 | `p adj` == 0.05), aes(
  x = reorder(ROI, desc(ROI)),
  y = diff,
  ymin = lwr,
  ymax = upr, color = Contrast)) +
  geom_hline(yintercept = 0,
             linetype = "11",
             colour = "grey60") +
  geom_pointrange( position = position_dodge(width = 0.3)) +
  #  geom_text(aes(label = str_c("p adj = ", signif(`p adj`, digits = 2))), nudge_x = 0.3) +
  coord_flip() + 
  labs(y =  "Differences in mean levels of Diagnostic", x = "ROI") + facet_grid(variable ~ agecorrection, labeller = labeller(agecorrection = agecorrection)) +
  theme_pubr() + 
  theme(axis.title = element_text(size = 11),
        axis.text = element_text(size = 10), text = element_text(size = 10))

aov_summary[order(aov_summary$diff, decreasing = TRUE),]
## # A tibble: 60 × 8
##    Contrast   diff     lwr    upr  `p adj` ROI            variable agecorrection
##    <chr>     <dbl>   <dbl>  <dbl>    <dbl> <chr>          <chr>    <chr>        
##  1 CTL-AD   0.0358 0.0260  0.0456 4.41e-14 Temporal Lobe  log[10]T no           
##  2 CTL-AD   0.0275 0.0181  0.0368 1.04e-10 Temporal Lobe  K        no           
##  3 CTL-AD   0.0237 0.0140  0.0334 8.08e- 8 Parietal Lobe  K        no           
##  4 CTL-AD   0.0233 0.0151  0.0314 3.45e-10 Hemisphere     log[10]T no           
##  5 CTL-AD   0.0232 0.0149  0.0316 1.06e- 9 Temporal Lobe  log[10]T yes          
##  6 MCI-AD   0.0221 0.0114  0.0328 6.19e- 6 Temporal Lobe  log[10]T no           
##  7 CTL-AD   0.0220 0.0149  0.0290 8.55e-12 Hemisphere     K        no           
##  8 CTL-AD   0.0217 0.0125  0.0309 2.18e- 7 Frontal Lobe   log[10]T no           
##  9 MCI-AD   0.0216 0.0109  0.0322 8.81e- 6 Parietal Lobe  K        no           
## 10 CTL-AD   0.0215 0.00989 0.0332 5.63e- 5 Occipital Lobe K        no           
## # … with 50 more rows
figs1
\label{fig:figureS1}Statistically significant (p<0.05) differences in mean levels with the 95% Confidence Interval of Diagnostics for K and log(AvgThickness), with (After age correction) and without (Raw data) age correction for the hemisphere and the four lobes. Multiple corrections were applied within each morphological feature and ROI.

Statistically significant (p<0.05) differences in mean levels with the 95% Confidence Interval of Diagnostics for K and log(AvgThickness), with (After age correction) and without (Raw data) age correction for the hemisphere and the four lobes. Multiple corrections were applied within each morphological feature and ROI.

7.0.1 Difference in K between diagnostics decrease with age?

We compared age intervals of 10 years to increase N at each comparison.

Linear model for visual inspection:

b <- lm(K ~ Age * ROI * Diagnostic, data = dados)
summary(b)
## 
## Call:
## lm(formula = K ~ Age * ROI * Diagnostic, data = dados)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.062579 -0.012117  0.000376  0.012462  0.061381 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     -4.175e-01  4.632e-02  -9.013   <2e-16 ***
## Age                             -9.139e-04  5.991e-04  -1.525   0.1274    
## ROIhemisphere                   -7.977e-02  6.551e-02  -1.218   0.2236    
## ROIO                             7.811e-02  6.551e-02   1.192   0.2333    
## ROIP                             1.091e-01  6.551e-02   1.665   0.0962 .  
## ROIT                             7.845e-02  6.551e-02   1.198   0.2313    
## DiagnosticMCI                   -2.951e-02  5.784e-02  -0.510   0.6100    
## DiagnosticCTL                   -1.502e-02  4.784e-02  -0.314   0.7535    
## Age:ROIhemisphere                2.715e-04  8.472e-04   0.320   0.7487    
## Age:ROIO                         4.846e-04  8.472e-04   0.572   0.5674    
## Age:ROIP                         5.873e-05  8.472e-04   0.069   0.9448    
## Age:ROIT                         2.245e-04  8.472e-04   0.265   0.7911    
## Age:DiagnosticMCI                5.356e-04  7.671e-04   0.698   0.4852    
## Age:DiagnosticCTL                3.757e-04  6.254e-04   0.601   0.5481    
## ROIhemisphere:DiagnosticMCI      4.889e-02  8.225e-02   0.594   0.5524    
## ROIO:DiagnosticMCI               3.163e-02  8.179e-02   0.387   0.6990    
## ROIP:DiagnosticMCI               5.957e-02  8.179e-02   0.728   0.4666    
## ROIT:DiagnosticMCI               1.041e-01  8.179e-02   1.273   0.2033    
## ROIhemisphere:DiagnosticCTL      2.221e-02  6.761e-02   0.328   0.7426    
## ROIO:DiagnosticCTL               3.178e-02  6.765e-02   0.470   0.6387    
## ROIP:DiagnosticCTL               1.799e-02  6.765e-02   0.266   0.7904    
## ROIT:DiagnosticCTL               4.475e-02  6.765e-02   0.662   0.5084    
## Age:ROIhemisphere:DiagnosticMCI -6.302e-04  1.091e-03  -0.578   0.5637    
## Age:ROIO:DiagnosticMCI          -4.052e-04  1.085e-03  -0.374   0.7088    
## Age:ROIP:DiagnosticMCI          -7.229e-04  1.085e-03  -0.666   0.5053    
## Age:ROIT:DiagnosticMCI          -1.413e-03  1.085e-03  -1.302   0.1931    
## Age:ROIhemisphere:DiagnosticCTL -2.606e-04  8.839e-04  -0.295   0.7681    
## Age:ROIO:DiagnosticCTL          -3.753e-04  8.845e-04  -0.424   0.6714    
## Age:ROIP:DiagnosticCTL          -2.050e-04  8.845e-04  -0.232   0.8168    
## Age:ROIT:DiagnosticCTL          -5.254e-04  8.845e-04  -0.594   0.5526    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01838 on 1192 degrees of freedom
## Multiple R-squared:   0.94,  Adjusted R-squared:  0.9386 
## F-statistic: 644.1 on 29 and 1192 DF,  p-value: < 2.2e-16
anova(b)
## Analysis of Variance Table
## 
## Response: K
##                      Df Sum Sq Mean Sq   F value    Pr(>F)    
## Age                   1 0.0591 0.05908  174.8143 < 2.2e-16 ***
## ROI                   4 6.2246 1.55614 4604.2704 < 2.2e-16 ***
## Diagnostic            2 0.0231 0.01157   34.2401 3.477e-15 ***
## Age:ROI               4 0.0029 0.00073    2.1533   0.07222 .  
## Age:Diagnostic        2 0.0003 0.00014    0.4199   0.65722    
## ROI:Diagnostic        8 0.0019 0.00024    0.7152   0.67830    
## Age:ROI:Diagnostic    8 0.0009 0.00011    0.3256   0.95649    
## Residuals          1192 0.4029 0.00034                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e <- allEffects(b)
e <- as.data.frame(e[[1]])
ggplot(e, aes(
  x = Age,
  y = fit,
  color = Diagnostic,
  ymin = lower,
  ymax = upper
)) +
  geom_pointrange() + geom_line() + theme_pubr() + facet_wrap(ROI ~ .) + labs(y = "K")

NSUBJ <-
  dados %>% group_by(Diagnostic, ROI, Age_interval10) %>% summarise(N = n_distinct(SUBJ))

a <- aov(K ~ Diagnostic * ROI * Age_interval10, data = dados)
a
## Call:
##    aov(formula = K ~ Diagnostic * ROI * Age_interval10, data = dados)
## 
## Terms:
##                 Diagnostic      ROI Age_interval10 Diagnostic:ROI
## Sum of Squares    0.058398 6.222467       0.036026       0.002912
## Deg. of Freedom          2        4              4              8
##                 Diagnostic:Age_interval10 ROI:Age_interval10
## Sum of Squares                   0.008345           0.004742
## Deg. of Freedom                         4                 16
##                 Diagnostic:ROI:Age_interval10 Residuals
## Sum of Squares                       0.005051  0.377744
## Deg. of Freedom                            16      1167
## 
## Residual standard error: 0.01799133
## 20 out of 75 effects not estimable
## Estimated effects may be unbalanced
summary(a)
##                                 Df Sum Sq Mean Sq  F value   Pr(>F)    
## Diagnostic                       2  0.058  0.0292   90.207  < 2e-16 ***
## ROI                              4  6.222  1.5556 4805.912  < 2e-16 ***
## Age_interval10                   4  0.036  0.0090   27.824  < 2e-16 ***
## Diagnostic:ROI                   8  0.003  0.0004    1.125    0.344    
## Diagnostic:Age_interval10        4  0.008  0.0021    6.446 3.95e-05 ***
## ROI:Age_interval10              16  0.005  0.0003    0.916    0.551    
## Diagnostic:ROI:Age_interval10   16  0.005  0.0003    0.975    0.482    
## Residuals                     1167  0.378  0.0003                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.TukeyHSD <-
  as.data.frame(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`)
Contrasts <-
  as.data.frame(rownames(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>%
  mutate(
    Contrast1 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 1],
    Contrast2 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 2]
  )
a.TukeyHSD <- a.TukeyHSD %>%
  mutate(
    Diagnostic.1 = str_split(
      a.TukeyHSD$Contrast1,
      pattern = ":",
      simplify = TRUE
    )[, 1],
    Diagnostic.2 = str_split(
      a.TukeyHSD$Contrast2,
      pattern = ":",
      simplify = TRUE
    )[, 1],
    ROI.1 = str_split(
      a.TukeyHSD$Contrast1,
      pattern = ":",
      simplify = TRUE
    )[, 2],
    ROI.2 = str_split(
      a.TukeyHSD$Contrast2,
      pattern = ":",
      simplify = TRUE
    )[, 2],
    Age.1 = str_split(
      a.TukeyHSD$Contrast1,
      pattern = ":",
      simplify = TRUE
    )[, 3],
    Age.2 = str_split(
      a.TukeyHSD$Contrast2,
      pattern = ":",
      simplify = TRUE
    )[, 3],
    Contrasts = str_c(Diagnostic.1, Diagnostic.2, sep = "-")
  ) %>% filter(ROI.1 == ROI.2 &
                 Age.1 == Age.2) %>% dplyr::select(-c(Contrast1 , Contrast2))

a.TukeyHSD$ROI.1 <- as.factor(a.TukeyHSD$ROI.1)
a.TukeyHSD$ROI.1 <- relevel(a.TukeyHSD$ROI.1, ref = "hemisphere")

figs2a <- ggplot(data = a.TukeyHSD, aes(
  x = Age.1,
  y = diff,
  ymin = lwr,
  ymax = upr,
  color = ROI.1
)) +
  geom_hline(yintercept = 0,
             linetype = "11",
             colour = "grey60") +
  geom_pointrange() + geom_line(aes(group = ROI.1)) + facet_wrap(Contrasts ~ .) +
  labs(y =  "Differences in mean of K", x = "Age [years]", color = "ROI") +
  theme_pubr() +
  theme(
    axis.title = element_text(size = 11),
    axis.text = element_text(size = 10),
    text = element_text(size = 10)
  )


a <- aov(K_age_decay ~ Diagnostic * ROI * Age_interval10, data = dados)
a
## Call:
##    aov(formula = K_age_decay ~ Diagnostic * ROI * Age_interval10, 
##     data = dados)
## 
## Terms:
##                 Diagnostic      ROI Age_interval10 Diagnostic:ROI
## Sum of Squares    0.023918 6.364823       0.009576       0.001663
## Deg. of Freedom          2        4              4              8
##                 Diagnostic:Age_interval10 ROI:Age_interval10
## Sum of Squares                   0.006937           0.003000
## Deg. of Freedom                         4                 16
##                 Diagnostic:ROI:Age_interval10 Residuals
## Sum of Squares                       0.004339  0.315550
## Deg. of Freedom                            16      1167
## 
## Residual standard error: 0.01644367
## 20 out of 75 effects not estimable
## Estimated effects may be unbalanced
summary(a)
##                                 Df Sum Sq Mean Sq  F value   Pr(>F)    
## Diagnostic                       2  0.024  0.0120   44.228  < 2e-16 ***
## ROI                              4  6.365  1.5912 5884.764  < 2e-16 ***
## Age_interval10                   4  0.010  0.0024    8.854 4.83e-07 ***
## Diagnostic:ROI                   8  0.002  0.0002    0.769    0.631    
## Diagnostic:Age_interval10        4  0.007  0.0017    6.414 4.18e-05 ***
## ROI:Age_interval10              16  0.003  0.0002    0.693    0.803    
## Diagnostic:ROI:Age_interval10   16  0.004  0.0003    1.003    0.451    
## Residuals                     1167  0.316  0.0003                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a.TukeyHSD <-
  as.data.frame(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`)
Contrasts <-
  as.data.frame(rownames(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>%
  mutate(
    Contrast1 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 1],
    Contrast2 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 2]
  )
a.TukeyHSD <- a.TukeyHSD %>%
  mutate(
    Diagnostic.1 = str_split(
      a.TukeyHSD$Contrast1,
      pattern = ":",
      simplify = TRUE
    )[, 1],
    Diagnostic.2 = str_split(
      a.TukeyHSD$Contrast2,
      pattern = ":",
      simplify = TRUE
    )[, 1],
    ROI.1 = str_split(
      a.TukeyHSD$Contrast1,
      pattern = ":",
      simplify = TRUE
    )[, 2],
    ROI.2 = str_split(
      a.TukeyHSD$Contrast2,
      pattern = ":",
      simplify = TRUE
    )[, 2],
    Age.1 = str_split(
      a.TukeyHSD$Contrast1,
      pattern = ":",
      simplify = TRUE
    )[, 3],
    Age.2 = str_split(
      a.TukeyHSD$Contrast2,
      pattern = ":",
      simplify = TRUE
    )[, 3],
    Contrasts = str_c(Diagnostic.1, Diagnostic.2, sep = "-")
  ) %>% filter(ROI.1 == ROI.2 &
                 Age.1 == Age.2) %>% dplyr::select(-c(Contrast1 , Contrast2))

a.TukeyHSD$ROI.1 <- as.factor(a.TukeyHSD$ROI.1)
a.TukeyHSD$ROI.1 <- relevel(a.TukeyHSD$ROI.1, ref = "hemisphere")

figs2b <- ggplot(data = a.TukeyHSD, aes(
  x = Age.1,
  y = diff,
  ymin = lwr,
  ymax = upr,
  color = ROI.1
)) +
  geom_hline(yintercept = 0,
             linetype = "11",
             colour = "grey60") +
  geom_pointrange() + geom_line(aes(group = ROI.1)) + facet_wrap(Contrasts ~ .) +
  labs(y =  "Differences in mean of K (age corrected)", x = "Age [years]", color = "ROI") +
  theme_pubr() +
  theme(
    axis.title = element_text(size = 11),
    axis.text = element_text(size = 10),
    text = element_text(size = 10)
  )

figs2 <-
  ggarrange(
    figs2a,
    figs2b,
    labels = c("A", "B"),
    common.legend = TRUE,
    legend = "top",
    nrow = 2,
    ncol = 1,
    font.label = list(size = 11)
  )

# ggsave(
#   "figs2.pdf",
#   plot = figs2,
#   dpi = 1200,
#   width = 17.8,
#   height = 22,
#   units = "cm",
#   device = "pdf"
# )

8 Figure S2

figs2
\label{fig:figureS2}Difference of means in pairwise comparison for AD-CTL, AD-MCI, and MCI-CTL in grouped by age in decades in each ROI for (A) K and (B) K after age correction. Bars represents 95% confidence interval. There is no statistical power, probably influenced by the small number of observations in each data point, to infer that the difference between diagnostics is more significant in younger adults.

Difference of means in pairwise comparison for AD-CTL, AD-MCI, and MCI-CTL in grouped by age in decades in each ROI for (A) K and (B) K after age correction. Bars represents 95% confidence interval. There is no statistical power, probably influenced by the small number of observations in each data point, to infer that the difference between diagnostics is more significant in younger adults.

8.0.1 Optimal cut-offs

## Method: maximize_boot_metric 
## Predictor: K 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.8442 180    26   154
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.5402        1.4341 0.8167      0.5769      0.8571 15 11 22 132
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5705655 -0.5554250 -0.5369054 -0.5277599 -0.5279802 -0.5160266
##       AD -0.5705655 -0.5675134 -0.5595947 -0.5523513 -0.5467767 -0.5315364
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.5553652 -0.5484493 -0.5346425 -0.5249779 -0.5248068 -0.5145646
##         95%       Max.         SD NAs
##  -0.5032479 -0.4974075 0.01602744   0
##  -0.5260303 -0.5225081 0.01543894   0
##          NA         NA         NA   0
##  -0.5028530 -0.4974075 0.01383501   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.55 -0.55   -0.54  -0.54 -0.54   -0.53 -0.53 -0.52 0.01
##              AUC_b  0.67  0.78    0.82   0.85  0.84    0.87  0.90  0.96 0.04
##            AUC_oob  0.60  0.74    0.81   0.85  0.84    0.88  0.93  1.00 0.06
##    sum_sens_spec_b  1.12  1.26    1.37   1.46  1.46    1.55  1.67  1.85 0.12
##  sum_sens_spec_oob  0.85  1.19    1.33   1.42  1.41    1.50  1.62  1.81 0.13
##              acc_b  0.48  0.65    0.72   0.78  0.78    0.85  0.92  0.96 0.08
##            acc_oob  0.49  0.62    0.71   0.78  0.77    0.84  0.90  0.97 0.09
##      sensitivity_b  0.40  0.50    0.58   0.63  0.66    0.71  0.88  1.00 0.11
##    sensitivity_oob  0.00  0.33    0.50   0.60  0.62    0.75  0.93  1.00 0.19
##      specificity_b  0.43  0.63    0.73   0.81  0.80    0.89  0.95  0.99 0.10
##    specificity_oob  0.36  0.58    0.71   0.81  0.79    0.89  0.96  1.00 0.12
##     cohens_kappa_b  0.08  0.16    0.25   0.33  0.36    0.47  0.65  0.83 0.15
##   cohens_kappa_oob -0.08  0.13    0.23   0.31  0.32    0.39  0.53  0.78 0.12
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0

## Method: maximize_boot_metric 
## Predictor: K 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.6232 220    66   154
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##           -0.5293        1.1753 0.6045      0.5455      0.6299 36 30 57 97
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5680537 -0.5499131 -0.5369054 -0.5269576 -0.5267026 -0.5160860
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.5680537 -0.5526269 -0.5402736 -0.5314849 -0.5311261 -0.5215015
##      CTL -0.5553652 -0.5484493 -0.5346425 -0.5249779 -0.5248068 -0.5145646
##         95%       Max.         SD NAs
##  -0.5046477 -0.4974075 0.01418700   0
##          NA         NA         NA   0
##  -0.5081453 -0.5055914 0.01411384   0
##  -0.5028530 -0.4974075 0.01383501   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.54 -0.54   -0.53  -0.53 -0.53   -0.52 -0.52 -0.51 0.00
##              AUC_b  0.49  0.56    0.60   0.62  0.62    0.65  0.69  0.78 0.04
##            AUC_oob  0.42  0.53    0.58   0.62  0.62    0.66  0.71  0.80 0.06
##    sum_sens_spec_b  0.95  1.06    1.13   1.19  1.18    1.23  1.31  1.50 0.07
##  sum_sens_spec_oob  0.78  0.98    1.08   1.14  1.14    1.21  1.30  1.48 0.10
##              acc_b  0.40  0.50    0.55   0.60  0.59    0.64  0.69  0.77 0.06
##            acc_oob  0.39  0.47    0.53   0.58  0.58    0.62  0.68  0.76 0.07
##      sensitivity_b  0.30  0.42    0.52   0.57  0.59    0.65  0.78  0.89 0.11
##    sensitivity_oob  0.13  0.32    0.46   0.56  0.56    0.67  0.80  1.00 0.15
##      specificity_b  0.23  0.41    0.50   0.61  0.60    0.69  0.78  0.88 0.12
##    specificity_oob  0.21  0.37    0.48   0.59  0.58    0.68  0.78  0.92 0.13
##     cohens_kappa_b -0.04  0.05    0.11   0.16  0.16    0.21  0.28  0.49 0.07
##   cohens_kappa_oob -0.15 -0.02    0.07   0.12  0.12    0.18  0.27  0.44 0.09
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0

## Method: maximize_boot_metric 
## Predictor: logAvgThickness 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.8492 180    26   154
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##            0.3877        1.4965 0.7333      0.7692      0.7273 20  6 42 112
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3493389 0.3647104 0.3833783 0.3960273 0.3958050 0.4074788 0.4267862
##       AD 0.3493389 0.3567966 0.3626697 0.3771104 0.3758867 0.3868844 0.3954810
##      MCI        NA        NA        NA        NA       NaN        NA        NA
##      CTL 0.3566807 0.3720598 0.3863418 0.3992158 0.3991678 0.4094520 0.4280006
##       Max.         SD NAs
##  0.4411069 0.01856467   0
##  0.4034812 0.01439800   0
##         NA         NA   0
##  0.4411069 0.01704522   0
## 
## Bootstrap summary: 
##           Variable Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint 0.38 0.38    0.39   0.39 0.39    0.39 0.39 0.40 0.00   0
##              AUC_b 0.74 0.79    0.83   0.85 0.85    0.87 0.90 0.95 0.03   0
##            AUC_oob 0.69 0.77    0.82   0.85 0.85    0.88 0.92 0.99 0.05   0
##    sum_sens_spec_b 1.22 1.38    1.46   1.52 1.52    1.57 1.65 1.83 0.09   0
##  sum_sens_spec_oob 1.04 1.24    1.37   1.46 1.46    1.54 1.65 1.92 0.12   0
##              acc_b 0.59 0.64    0.70   0.73 0.74    0.78 0.86 0.93 0.06   0
##            acc_oob 0.55 0.61    0.68   0.72 0.72    0.77 0.83 0.93 0.07   0
##      sensitivity_b 0.53 0.66    0.73   0.78 0.78    0.83 0.91 1.00 0.08   0
##    sensitivity_oob 0.17 0.44    0.62   0.75 0.73    0.86 1.00 1.00 0.17   0
##      specificity_b 0.56 0.62    0.68   0.73 0.73    0.79 0.87 0.95 0.08   0
##    specificity_oob 0.48 0.57    0.66   0.72 0.72    0.79 0.89 0.96 0.10   0
##     cohens_kappa_b 0.11 0.20    0.27   0.33 0.34    0.39 0.51 0.72 0.10   0
##   cohens_kappa_oob 0.04 0.15    0.23   0.29 0.29    0.35 0.45 0.76 0.09   0

## Method: maximize_boot_metric 
## Predictor: logAvgThickness 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.6401 220    66   154
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##            0.3994        1.1688 0.5455      0.6818       0.487 45 21 79 75
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3516182 0.3687896 0.3851133 0.3969891 0.3965573 0.4074223 0.4258681
##       AD        NA        NA        NA        NA       NaN        NA        NA
##      MCI 0.3516182 0.3675042 0.3802191 0.3905426 0.3904661 0.4003543 0.4150275
##      CTL 0.3566807 0.3720598 0.3863418 0.3992158 0.3991678 0.4094520 0.4280006
##       Max.         SD NAs
##  0.4411069 0.01693813   0
##         NA         NA   0
##  0.4220982 0.01513035   0
##  0.4411069 0.01704522   0
## 
## Bootstrap summary: 
##           Variable  Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.39 0.39    0.40   0.40 0.40    0.40 0.40 0.41 0.00   0
##              AUC_b  0.51 0.57    0.61   0.64 0.64    0.66 0.70 0.76 0.04   0
##            AUC_oob  0.50 0.55    0.61   0.64 0.64    0.68 0.73 0.80 0.05   0
##    sum_sens_spec_b  0.95 1.09    1.16   1.20 1.20    1.25 1.32 1.42 0.07   0
##  sum_sens_spec_oob  0.86 1.02    1.11   1.17 1.17    1.23 1.33 1.44 0.09   0
##              acc_b  0.39 0.48    0.54   0.58 0.57    0.61 0.65 0.71 0.06   0
##            acc_oob  0.39 0.46    0.52   0.56 0.56    0.60 0.65 0.73 0.06   0
##      sensitivity_b  0.37 0.52    0.61   0.67 0.67    0.75 0.83 0.93 0.10   0
##    sensitivity_oob  0.24 0.43    0.56   0.65 0.66    0.76 0.87 1.00 0.13   0
##      specificity_b  0.19 0.35    0.46   0.54 0.53    0.61 0.70 0.78 0.10   0
##    specificity_oob  0.18 0.32    0.43   0.52 0.52    0.60 0.70 0.81 0.11   0
##     cohens_kappa_b -0.04 0.07    0.13   0.17 0.17    0.21 0.27 0.37 0.06   0
##   cohens_kappa_oob -0.07 0.02    0.09   0.13 0.14    0.19 0.28 0.38 0.08   0

8.0.1.1 Deaged

cpK <-
    cutpointr(
        filter(dados_hemi_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
        K_age_decay,
        Diagnostic,
        pos_class = "AD",
        neg_class = "CTL",
        method = maximize_boot_metric,
        metric = sum_sens_spec,
        na.rm = TRUE,
        boot_runs = 1000,
    use_midpoints = TRUE)
summary(cpK)
## Method: maximize_boot_metric 
## Predictor: K_age_decay 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.7852 180    26   154
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.5196        1.4081 0.7944      0.5769      0.8312 15 11 26 128
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5423463 -0.5317798 -0.5170820 -0.5061175 -0.5082363 -0.4987331
##       AD -0.5423463 -0.5397941 -0.5315661 -0.5261094 -0.5207321 -0.5064586
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.5324581 -0.5259456 -0.5149816 -0.5051845 -0.5061266 -0.4980014
##         95%       Max.         SD NAs
##  -0.4887326 -0.4795903 0.01320578   0
##  -0.5032434 -0.5026811 0.01356594   0
##          NA         NA         NA   0
##  -0.4878314 -0.4795903 0.01195587   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.53 -0.53   -0.52  -0.52 -0.52   -0.51 -0.51 -0.50 0.01
##              AUC_b  0.62  0.70    0.75   0.78  0.78    0.81  0.86  0.93 0.05
##            AUC_oob  0.52  0.68    0.75   0.79  0.79    0.83  0.89  0.99 0.06
##    sum_sens_spec_b  0.93  1.15    1.28   1.40  1.39    1.49  1.63  1.83 0.15
##  sum_sens_spec_oob  0.76  1.16    1.28   1.36  1.36    1.44  1.54  1.70 0.12
##              acc_b  0.46  0.56    0.68   0.78  0.76    0.85  0.91  0.96 0.11
##            acc_oob  0.41  0.53    0.67   0.78  0.75    0.83  0.89  0.94 0.11
##      sensitivity_b  0.40  0.48    0.55   0.59  0.60    0.65  0.79  1.00 0.09
##    sensitivity_oob  0.10  0.30    0.45   0.56  0.58    0.67  0.90  1.00 0.18
##      specificity_b  0.43  0.53    0.69   0.81  0.79    0.89  0.95  0.99 0.13
##    specificity_oob  0.34  0.47    0.68   0.81  0.78    0.90  0.96  1.00 0.15
##     cohens_kappa_b -0.04  0.08    0.17   0.30  0.32    0.44  0.63  0.83 0.18
##   cohens_kappa_oob -0.08  0.10    0.19   0.27  0.28    0.37  0.48  0.72 0.12
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
plot(cpK)

cpK_MCI <-
    cutpointr(
        filter(dados_hemi_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
        K_age_decay,
        Diagnostic,
        pos_class = "MCI",
        neg_class = "CTL",
        method = maximize_boot_metric,
        metric = sum_sens_spec,
        na.rm = TRUE,
        boot_runs = 1000,
    use_midpoints = TRUE)
summary(cpK_MCI)
## Method: maximize_boot_metric 
## Predictor: K_age_decay 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.5669 220    66   154
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.5101        1.1494 0.6045         0.5      0.6494 33 33 54 100
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5385238 -0.5271766 -0.5158942 -0.5059702 -0.5070307 -0.4980455
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.5385238 -0.5288096 -0.5176042 -0.5104862 -0.5091402 -0.4998434
##      CTL -0.5324581 -0.5259456 -0.5149816 -0.5051845 -0.5061266 -0.4980014
##         95%       Max.         SD NAs
##  -0.4886819 -0.4795903 0.01225196   0
##          NA         NA         NA   0
##  -0.4892321 -0.4849284 0.01276075   0
##  -0.4878314 -0.4795903 0.01195587   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.52 -0.52   -0.51  -0.51 -0.51   -0.51 -0.50 -0.50 0.00
##              AUC_b  0.41  0.50    0.54   0.57  0.57    0.60  0.64  0.71 0.04
##            AUC_oob  0.34  0.47    0.53   0.57  0.57    0.60  0.66  0.79 0.06
##    sum_sens_spec_b  0.88  1.01    1.08   1.14  1.14    1.20  1.28  1.41 0.08
##  sum_sens_spec_oob  0.79  0.95    1.03   1.10  1.10    1.17  1.27  1.46 0.10
##              acc_b  0.37  0.51    0.56   0.60  0.60    0.63  0.68  0.74 0.05
##            acc_oob  0.39  0.49    0.55   0.59  0.58    0.62  0.68  0.74 0.06
##      sensitivity_b  0.13  0.29    0.44   0.51  0.50    0.57  0.67  0.80 0.11
##    sensitivity_oob  0.04  0.23    0.39   0.48  0.47    0.56  0.67  0.91 0.13
##      specificity_b  0.19  0.48    0.58   0.65  0.64    0.71  0.79  0.93 0.09
##    specificity_oob  0.25  0.45    0.56   0.64  0.63    0.71  0.81  0.93 0.11
##     cohens_kappa_b -0.09  0.01    0.08   0.13  0.13    0.18  0.25  0.38 0.08
##   cohens_kappa_oob -0.22 -0.05    0.03   0.09  0.09    0.15  0.24  0.37 0.09
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
plot(cpK_MCI)

cpT <-
    cutpointr(
        filter(dados_hemi_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
       logAvgThickness_age_decay,
        Diagnostic,
        pos_class = "AD",
        neg_class = "CTL",
        method = maximize_boot_metric,
        metric = sum_sens_spec,
        na.rm = TRUE,
        boot_runs = 1000,
    use_midpoints = TRUE)
summary(cpT)
## Method: maximize_boot_metric 
## Predictor: logAvgThickness_age_decay 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.7248 180    26   154
## 
##  optimal_cutpoint sum_sens_spec  acc sensitivity specificity tp fn fp  tn
##            0.4227        1.2922 0.75         0.5      0.7922 13 13 32 122
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3915353 0.4087752 0.4227995 0.4301048 0.4318351 0.4421783 0.4588600
##       AD 0.3915353 0.4010329 0.4116461 0.4226828 0.4214529 0.4291104 0.4418508
##      MCI        NA        NA        NA        NA       NaN        NA        NA
##      CTL 0.4001589 0.4134574 0.4238657 0.4314060 0.4335880 0.4435058 0.4590177
##       Max.         SD NAs
##  0.4628195 0.01460961   0
##  0.4468100 0.01378867   0
##         NA         NA   0
##  0.4628195 0.01404404   0
## 
## Bootstrap summary: 
##           Variable  Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.41 0.42    0.42   0.42 0.42    0.43 0.43 0.44 0.00   0
##              AUC_b  0.47 0.63    0.69   0.73 0.72    0.76 0.81 0.89 0.06   0
##            AUC_oob  0.30 0.61    0.67   0.72 0.72    0.77 0.85 0.96 0.08   0
##    sum_sens_spec_b  0.89 1.14    1.26   1.34 1.33    1.40 1.50 1.64 0.11   0
##  sum_sens_spec_oob  0.78 1.04    1.18   1.27 1.27    1.36 1.49 1.69 0.14   0
##              acc_b  0.32 0.53    0.66   0.71 0.71    0.77 0.83 0.89 0.09   0
##            acc_oob  0.33 0.54    0.64   0.70 0.69    0.76 0.82 0.89 0.09   0
##      sensitivity_b  0.31 0.44    0.54   0.61 0.61    0.68 0.80 0.92 0.11   0
##    sensitivity_oob  0.00 0.25    0.40   0.55 0.56    0.70 0.89 1.00 0.19   0
##      specificity_b  0.25 0.51    0.66   0.73 0.72    0.82 0.88 0.94 0.12   0
##    specificity_oob  0.21 0.50    0.64   0.73 0.72    0.81 0.88 0.98 0.13   0
##     cohens_kappa_b -0.05 0.07    0.16   0.23 0.23    0.29 0.40 0.55 0.10   0
##   cohens_kappa_oob -0.14 0.03    0.12   0.18 0.18    0.24 0.35 0.45 0.10   0
plot(cpT)

cpT_MCI <-
    cutpointr(
        filter(dados_hemi_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
       logAvgThickness_age_decay,
        Diagnostic,
        pos_class = "MCI",
        neg_class = "CTL",
        method = maximize_boot_metric,
        metric = sum_sens_spec,
        na.rm = TRUE,
        boot_runs = 1000,
    use_midpoints = TRUE)
summary(cpT_MCI)
## Method: maximize_boot_metric 
## Predictor: logAvgThickness_age_decay 
## Outcome: Diagnostic 
## Direction: <= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.5501 220    66   154
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##            0.4343        1.0108 0.4773      0.5758      0.4351 38 28 87 67
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.4001589 0.4124859 0.4231040 0.4308261 0.4327380 0.4430464 0.4571460
##       AD        NA        NA        NA        NA       NaN        NA        NA
##      MCI 0.4024658 0.4102051 0.4209982 0.4294644 0.4307546 0.4406655 0.4491568
##      CTL 0.4001589 0.4134574 0.4238657 0.4314060 0.4335880 0.4435058 0.4590177
##       Max.         SD NAs
##  0.4628195 0.01359485   0
##         NA         NA   0
##  0.4557856 0.01235857   0
##  0.4628195 0.01404404   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.42  0.43    0.43   0.44 0.44    0.44 0.45 0.45 0.01   0
##              AUC_b  0.39  0.48    0.52   0.55 0.55    0.58 0.62 0.70 0.04   0
##            AUC_oob  0.38  0.46    0.51   0.55 0.55    0.59 0.64 0.72 0.05   0
##    sum_sens_spec_b  0.78  0.93    1.01   1.07 1.07    1.12 1.20 1.33 0.08   0
##  sum_sens_spec_oob  0.68  0.87    0.96   1.03 1.02    1.09 1.17 1.34 0.09   0
##              acc_b  0.31  0.38    0.42   0.47 0.48    0.53 0.63 0.73 0.08   0
##            acc_oob  0.27  0.36    0.41   0.46 0.46    0.51 0.59 0.69 0.07   0
##      sensitivity_b  0.29  0.45    0.55   0.63 0.66    0.77 0.91 1.00 0.15   0
##    sensitivity_oob  0.16  0.32    0.48   0.62 0.63    0.79 0.95 1.00 0.20   0
##      specificity_b  0.10  0.19    0.28   0.38 0.41    0.52 0.70 0.87 0.16   0
##    specificity_oob  0.07  0.16    0.27   0.38 0.40    0.52 0.69 0.82 0.16   0
##     cohens_kappa_b -0.16 -0.05    0.01   0.05 0.06    0.10 0.19 0.32 0.07   0
##   cohens_kappa_oob -0.23 -0.11   -0.03   0.02 0.02    0.06 0.13 0.29 0.07   0
plot(cpT_MCI)

cutpoint_a_deaged <- ggplot(dados_hemi_v1, aes(x = K_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
  geom_density() +
  geom_vline(data = cpK, aes(xintercept = optimal_cutpoint), linetype = "dashed") + 
    geom_vline(data = cpK_MCI, aes(xintercept = optimal_cutpoint), linetype = "dotted") + 
  theme_pubr() +
  guides(alpha = "none", linetype = "none") +
  theme(axis.title = element_text(size = 11),
    axis.text = element_text(size = 10), text = element_text(size = 10)) +
  labs(x = "K (After age correction)")  + scale_x_continuous(
              labels = scales::number_format(accuracy = 0.01), limits=c(-0.59,-0.48), breaks = c(-0.58, -0.55, -0.52, -0.49))
# cutpoint_a_deaged

cutpoint_b_deaged <- ggplot(dados_hemi_v1, aes(x = logAvgThickness_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
  geom_density() +
  geom_vline(data = cpT, aes(xintercept = optimal_cutpoint), linetype = "dashed") + 
  geom_vline(data = cpT_MCI, aes(xintercept = optimal_cutpoint), linetype = "dotted") + 
  theme_pubr() +
  guides(alpha = "none", linetype = "none") +
  theme(axis.title = element_text(size = 11),
    axis.text = element_text(size = 10), text = element_text(size = 10), legend.position = "none") +
  labs(x = expression('log'[10]*'T '*('After age correction'))) +
 scale_x_continuous(limits=c(0.32,0.48), n.breaks = 4)

fig_cutpoint_deaged_alt <- ggarrange(cutpoint_a, cutpoint_a_deaged, cutpoint_b, cutpoint_b_deaged, labels = c("A", "B", "C", "D"),  ncol = 1, nrow = 4, font.label = list(size = 11), common.legend = TRUE, legend = "top")

9 Figure 2

fig_cutpoint_deaged_alt
\label{fig:figure2}Optimal cut-off (maximum sensitivity + specificity) for K and Average Cortical Thickness including results with removed age effect (age correction). AD in red (N = 13), MCI in green (N = 33), and Cognitive Unimpaired Controls (CTL) in blue (N = 77). The dashed line represents optimal cut-off to discriminate AD and CTL, and the dotted line represents optimal cut-off to discriminate MCI and CTL. ACC - accuracy, SPEC - specificity, and SENS - sensibility. (A) The optimal cut-off for the CTL-AD contrast is -0.54 and CTL-MCI, -0.53. (B) The optimal cut-off for CTL-AD = -0.52 and CTL-MCI = -0.51. (C) The optimal cut-off for CTL-AD = 0.39 mm and CTL-MCI = 0.40 mm. (D) The optimal cut-off for CTL-AD = 0.43 mm and CTL-MCI = 0.44 mm.

Optimal cut-off (maximum sensitivity + specificity) for K and Average Cortical Thickness including results with removed age effect (age correction). AD in red (N = 13), MCI in green (N = 33), and Cognitive Unimpaired Controls (CTL) in blue (N = 77). The dashed line represents optimal cut-off to discriminate AD and CTL, and the dotted line represents optimal cut-off to discriminate MCI and CTL. ACC - accuracy, SPEC - specificity, and SENS - sensibility. (A) The optimal cut-off for the CTL-AD contrast is -0.54 and CTL-MCI, -0.53. (B) The optimal cut-off for CTL-AD = -0.52 and CTL-MCI = -0.51. (C) The optimal cut-off for CTL-AD = 0.39 mm and CTL-MCI = 0.40 mm. (D) The optimal cut-off for CTL-AD = 0.43 mm and CTL-MCI = 0.44 mm.

9.0.1 Lobes

9.0.1.1 Cut point - lobes

10 Table S2

dados_lobos_v1$subgroup <- dados_lobos_v1$ROI

cpK <-
  cutpointr(
    filter(dados_lobos_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
    K_corrected,
    Diagnostic,
    ROI,
    pos_class = "AD",
    neg_class = "CTL",
    method = maximize_boot_metric,
    metric = sum_sens_spec,
    na.rm = TRUE,
    boot_runs = 1000,
    use_midpoints = TRUE
  )
summary(cpK)
## Method: maximize_boot_metric 
## Predictor: K_corrected 
## Outcome: Diagnostic 
## Direction: <= 
## Subgroups: F, P, T, O 
## Nr. of bootstraps: 1000 
## 
## Subgroup: F 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.7511 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.5505        1.3227 0.6667      0.6538      0.6689 17  9 50 101
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5948952 -0.5741638 -0.5569396 -0.5445030 -0.5448128 -0.5314285
##       AD -0.5948952 -0.5918458 -0.5743711 -0.5572738 -0.5604638 -0.5461758
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.5777982 -0.5689791 -0.5558189 -0.5411732 -0.5421180 -0.5284866
##         95%       Max.         SD NAs
##  -0.5183209 -0.5040702 0.01828324   0
##  -0.5310599 -0.5236191 0.01992406   0
##          NA         NA         NA   0
##  -0.5176837 -0.5040702 0.01662191   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.57 -0.56   -0.56  -0.55 -0.55   -0.55 -0.54 -0.54 0.01
##              AUC_b  0.52  0.66    0.71   0.75  0.75    0.79  0.83  0.90 0.05
##            AUC_oob  0.44  0.63    0.71   0.75  0.75    0.80  0.87  0.99 0.07
##    sum_sens_spec_b  0.97  1.17    1.28   1.35  1.35    1.43  1.53  1.63 0.11
##  sum_sens_spec_oob  0.75  1.05    1.20   1.30  1.30    1.39  1.53  1.75 0.14
##              acc_b  0.40  0.58    0.65   0.71  0.71    0.76  0.84  0.95 0.08
##            acc_oob  0.42  0.54    0.64   0.70  0.69    0.76  0.83  0.89 0.09
##      sensitivity_b  0.32  0.43    0.55   0.64  0.63    0.71  0.81  0.97 0.12
##    sensitivity_oob  0.00  0.25    0.44   0.60  0.59    0.73  0.89  1.00 0.20
##      specificity_b  0.36  0.55    0.65   0.72  0.72    0.80  0.89  0.98 0.11
##    specificity_oob  0.33  0.50    0.63   0.72  0.71    0.79  0.90  0.98 0.12
##     cohens_kappa_b -0.01  0.10    0.18   0.24  0.25    0.30  0.43  0.63 0.10
##   cohens_kappa_oob -0.16  0.03    0.13   0.19  0.19    0.26  0.37  0.52 0.10
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: P 
## -------------------------------------------------------------------------------- 
##    AUC   n n_pos n_neg
##  0.824 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.5222        1.4671 0.7627      0.6923      0.7748 18  8 34 117
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5753676 -0.5497660 -0.5277313 -0.5107007 -0.5114334 -0.4938295
##       AD -0.5753676 -0.5711317 -0.5484500 -0.5313049 -0.5344850 -0.5217755
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.5551769 -0.5406598 -0.5193854 -0.5069022 -0.5074642 -0.4923347
##         95%       Max.         SD NAs
##  -0.4770092 -0.4638806 0.02220156   0
##  -0.5046080 -0.4986455 0.02042450   0
##          NA         NA         NA   0
##  -0.4768356 -0.4638806 0.02002138   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.54 -0.53   -0.52  -0.52 -0.52   -0.52 -0.51 -0.50 0.00
##              AUC_b  0.63  0.75    0.80   0.83  0.83    0.85  0.89  0.92 0.04
##            AUC_oob  0.61  0.72    0.79   0.82  0.82    0.86  0.91  0.97 0.06
##    sum_sens_spec_b  1.13  1.33    1.45   1.52  1.51    1.57  1.66  1.77 0.10
##  sum_sens_spec_oob  0.97  1.22    1.37   1.46  1.45    1.55  1.66  1.85 0.13
##              acc_b  0.46  0.63    0.73   0.77  0.75    0.80  0.83  0.88 0.06
##            acc_oob  0.47  0.62    0.71   0.75  0.74    0.78  0.83  0.91 0.06
##      sensitivity_b  0.52  0.62    0.71   0.76  0.75    0.80  0.87  0.97 0.07
##    sensitivity_oob  0.17  0.44    0.60   0.71  0.71    0.82  1.00  1.00 0.16
##      specificity_b  0.40  0.61    0.72   0.77  0.75    0.80  0.84  0.91 0.07
##    specificity_oob  0.39  0.58    0.71   0.76  0.75    0.80  0.86  0.98 0.09
##     cohens_kappa_b  0.06  0.18    0.29   0.35  0.35    0.41  0.48  0.60 0.09
##   cohens_kappa_oob -0.01  0.15    0.24   0.30  0.31    0.37  0.47  0.70 0.10
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: T 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.8097 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##           -0.5108        1.4249 0.6723      0.7692      0.6556 20  6 52 99
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5947243 -0.5401430 -0.5198351 -0.5067115 -0.5066949 -0.4911627
##       AD -0.5947243 -0.5671082 -0.5379738 -0.5261631 -0.5287671 -0.5129811
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.5579907 -0.5320530 -0.5173996 -0.5022748 -0.5028944 -0.4888990
##         95%       Max.         SD NAs
##  -0.4728609 -0.4601226 0.02184019   0
##  -0.4992148 -0.4960854 0.02298717   0
##          NA         NA         NA   0
##  -0.4718815 -0.4601226 0.01930335   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.53 -0.52   -0.52  -0.51 -0.51   -0.51 -0.51 -0.50 0.01
##              AUC_b  0.66  0.74    0.79   0.81  0.81    0.84  0.87  0.92 0.04
##            AUC_oob  0.44  0.71    0.78   0.81  0.81    0.85  0.90  0.98 0.06
##    sum_sens_spec_b  1.14  1.31    1.39   1.45  1.45    1.51  1.59  1.73 0.09
##  sum_sens_spec_oob  0.70  1.15    1.29   1.39  1.38    1.47  1.60  1.81 0.14
##              acc_b  0.48  0.60    0.66   0.70  0.70    0.74  0.82  0.92 0.07
##            acc_oob  0.42  0.57    0.64   0.69  0.69    0.74  0.81  0.90 0.07
##      sensitivity_b  0.44  0.58    0.68   0.76  0.75    0.83  0.90  1.00 0.10
##    sensitivity_oob  0.00  0.36    0.56   0.70  0.69    0.82  1.00  1.00 0.19
##      specificity_b  0.40  0.56    0.64   0.69  0.70    0.75  0.85  0.95 0.08
##    specificity_oob  0.32  0.53    0.62   0.68  0.69    0.76  0.87  1.00 0.10
##     cohens_kappa_b  0.08  0.16    0.22   0.27  0.28    0.33  0.43  0.64 0.08
##   cohens_kappa_oob -0.11  0.09    0.18   0.23  0.23    0.29  0.39  0.51 0.09
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: O 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6882 177    26   151
## 
##  optimal_cutpoint sum_sens_spec   acc sensitivity specificity tp fn fp  tn
##           -0.4975        1.4047 0.791      0.5769      0.8278 15 11 26 125
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5532809 -0.5253072 -0.4962470 -0.4817953 -0.4829495 -0.4698824
##       AD -0.5532809 -0.5338382 -0.5230440 -0.5001447 -0.4975855 -0.4713651
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.5299648 -0.5143920 -0.4932941 -0.4800622 -0.4804294 -0.4699315
##         95%       Max.         SD NAs
##  -0.4463241 -0.4209974 0.02293166   0
##  -0.4525778 -0.4390378 0.02999238   0
##          NA         NA         NA   0
##  -0.4463146 -0.4209974 0.02058064   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.52 -0.51   -0.50  -0.50 -0.50   -0.50 -0.49 -0.49 0.00
##              AUC_b  0.45  0.57    0.64   0.69  0.69    0.74  0.80  0.88 0.07
##            AUC_oob  0.40  0.52    0.62   0.69  0.69    0.76  0.85  0.98 0.10
##    sum_sens_spec_b  1.06  1.20    1.31   1.39  1.39    1.46  1.55  1.71 0.11
##  sum_sens_spec_oob  0.84  1.09    1.23   1.32  1.33    1.42  1.56  1.82 0.14
##              acc_b  0.60  0.70    0.76   0.80  0.79    0.83  0.86  0.92 0.05
##            acc_oob  0.54  0.67    0.74   0.78  0.78    0.82  0.86  0.92 0.06
##      sensitivity_b  0.20  0.37    0.48   0.55  0.55    0.64  0.74  0.88 0.11
##    sensitivity_oob  0.00  0.25    0.40   0.50  0.50    0.62  0.75  1.00 0.16
##      specificity_b  0.61  0.72    0.80   0.84  0.83    0.88  0.92  0.97 0.06
##    specificity_oob  0.54  0.69    0.78   0.83  0.82    0.88  0.94  0.98 0.08
##     cohens_kappa_b  0.04  0.16    0.25   0.32  0.32    0.39  0.47  0.64 0.10
##   cohens_kappa_oob -0.13  0.07    0.18   0.27  0.27    0.35  0.46  0.66 0.12
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
plot(cpK)

cpK_MCI <-
  cutpointr(
    filter(dados_lobos_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
    K_corrected,
    Diagnostic,
    ROI,
    pos_class = "MCI",
    neg_class = "CTL",
    method = maximize_boot_metric,
    metric = sum_sens_spec,
    na.rm = TRUE,
    boot_runs = 1000,
    use_midpoints = TRUE
  )
summary(cpK_MCI)
## Method: maximize_boot_metric 
## Predictor: K_corrected 
## Outcome: Diagnostic 
## Direction: <= 
## Subgroups: F, P, T, O 
## Nr. of bootstraps: 1000 
## 
## Subgroup: F 
## -------------------------------------------------------------------------------- 
##    AUC   n n_pos n_neg
##  0.587 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##           -0.5412        1.1648 0.5509      0.6615      0.5033 43 22 75 76
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5781076 -0.5708354 -0.5559783 -0.5441751 -0.5436111 -0.5312231
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.5781076 -0.5746358 -0.5580436 -0.5484470 -0.5470797 -0.5369166
##      CTL -0.5777982 -0.5689791 -0.5558189 -0.5411732 -0.5421180 -0.5284866
##         95%       Max.         SD NAs
##  -0.5179243 -0.5040702 0.01673165   0
##          NA         NA         NA   0
##  -0.5201961 -0.5115329 0.01659585   0
##  -0.5176837 -0.5040702 0.01662191   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.56 -0.55   -0.55  -0.54 -0.54   -0.54 -0.53 -0.53 0.00
##              AUC_b  0.45  0.52    0.56   0.59  0.59    0.61  0.66  0.74 0.04
##            AUC_oob  0.37  0.50    0.55   0.59  0.59    0.63  0.68  0.76 0.06
##    sum_sens_spec_b  0.89  1.03    1.10   1.16  1.16    1.21  1.29  1.41 0.08
##  sum_sens_spec_oob  0.74  0.96    1.06   1.12  1.12    1.18  1.28  1.38 0.10
##              acc_b  0.40  0.46    0.52   0.56  0.55    0.59  0.63  0.70 0.05
##            acc_oob  0.32  0.44    0.50   0.54  0.54    0.58  0.63  0.71 0.06
##      sensitivity_b  0.21  0.46    0.58   0.65  0.64    0.71  0.79  0.89 0.10
##    sensitivity_oob  0.15  0.39    0.53   0.62  0.61    0.71  0.83  1.00 0.13
##      specificity_b  0.26  0.34    0.46   0.52  0.52    0.58  0.68  0.83 0.10
##    specificity_oob  0.16  0.30    0.43   0.51  0.50    0.58  0.69  0.85 0.11
##     cohens_kappa_b -0.12  0.03    0.08   0.13  0.13    0.17  0.24  0.35 0.07
##   cohens_kappa_oob -0.25 -0.04    0.04   0.10  0.10    0.15  0.23  0.34 0.08
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: P 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.4995 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##           -0.4988        1.0149 0.4583      0.6308      0.3841 41 24 93 58
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5755226 -0.5408819 -0.5192216 -0.5062234 -0.5077524 -0.4923294
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.5755226 -0.5460118 -0.5189791 -0.5047562 -0.5084220 -0.4923564
##      CTL -0.5551769 -0.5406598 -0.5193854 -0.5069022 -0.5074642 -0.4923347
##         95%       Max.         SD NAs
##  -0.4770073 -0.4638806 0.02041888   0
##          NA         NA         NA   0
##  -0.4825298 -0.4661993 0.02145856   0
##  -0.4768356 -0.4638806 0.02002138   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.55 -0.53   -0.51  -0.50 -0.51   -0.50 -0.49 -0.48 0.01
##              AUC_b  0.34  0.43    0.47   0.50  0.50    0.53  0.58  0.62 0.04
##            AUC_oob  0.30  0.40    0.46   0.50  0.50    0.54  0.60  0.71 0.06
##    sum_sens_spec_b  0.77  0.89    0.97   1.02  1.02    1.08  1.15  1.25 0.08
##  sum_sens_spec_oob  0.65  0.81    0.91   0.98  0.97    1.04  1.13  1.39 0.10
##              acc_b  0.26  0.38    0.44   0.49  0.50    0.55  0.63  0.72 0.08
##            acc_oob  0.19  0.35    0.41   0.47  0.48    0.54  0.64  0.78 0.09
##      sensitivity_b  0.04  0.18    0.38   0.57  0.54    0.71  0.84  0.99 0.21
##    sensitivity_oob  0.00  0.12    0.33   0.52  0.51    0.68  0.87  1.00 0.23
##      specificity_b  0.12  0.18    0.34   0.45  0.48    0.60  0.82  0.99 0.19
##    specificity_oob  0.04  0.15    0.31   0.44  0.46    0.60  0.83  1.00 0.21
##     cohens_kappa_b -0.19 -0.10   -0.03   0.01  0.01    0.06  0.12  0.20 0.06
##   cohens_kappa_oob -0.30 -0.17   -0.08  -0.02 -0.02    0.03  0.10  0.25 0.08
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: T 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6738 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##           -0.5074        1.2223 0.5972      0.6462      0.5762 42 23 64 87
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5579907 -0.5357395 -0.5218213 -0.5066437 -0.5063702 -0.4918393
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.5492814 -0.5407649 -0.5278604 -0.5155029 -0.5144448 -0.4986739
##      CTL -0.5579907 -0.5320530 -0.5173996 -0.5022748 -0.5028944 -0.4888990
##         95%       Max.         SD NAs
##  -0.4746984 -0.4601226 0.01961174   0
##          NA         NA         NA   0
##  -0.4811155 -0.4745659 0.01800097   0
##  -0.4718815 -0.4601226 0.01930335   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.52 -0.52   -0.51  -0.51 -0.51   -0.50 -0.50 -0.49 0.01
##              AUC_b  0.56  0.60    0.65   0.67  0.67    0.70  0.74  0.79 0.04
##            AUC_oob  0.45  0.58    0.64   0.67  0.67    0.71  0.76  0.82 0.05
##    sum_sens_spec_b  1.01  1.13    1.20   1.25  1.25    1.30  1.37  1.53 0.07
##  sum_sens_spec_oob  0.90  1.05    1.14   1.21  1.21    1.27  1.36  1.46 0.09
##              acc_b  0.46  0.52    0.58   0.62  0.62    0.66  0.71  0.78 0.06
##            acc_oob  0.39  0.50    0.56   0.60  0.60    0.64  0.69  0.77 0.06
##      sensitivity_b  0.35  0.50    0.58   0.65  0.65    0.71  0.79  0.94 0.09
##    sensitivity_oob  0.11  0.39    0.50   0.61  0.61    0.71  0.83  1.00 0.14
##      specificity_b  0.30  0.42    0.54   0.61  0.61    0.69  0.77  0.86 0.11
##    specificity_oob  0.22  0.38    0.51   0.61  0.60    0.68  0.78  0.93 0.12
##     cohens_kappa_b  0.00  0.11    0.17   0.22  0.22    0.27  0.35  0.47 0.07
##   cohens_kappa_oob -0.10  0.05    0.12   0.18  0.18    0.23  0.32  0.42 0.08
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: O 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.5561 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##             -0.48        1.1428 0.5417      0.6462      0.4967 42 23 76 75
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.5299648 -0.5151736 -0.4939268 -0.4814026 -0.4814872 -0.4706468
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.5197871 -0.5148114 -0.4950804 -0.4847680 -0.4839446 -0.4742901
##      CTL -0.5299648 -0.5143920 -0.4932941 -0.4800622 -0.4804294 -0.4699315
##         95%       Max.         SD NAs
##  -0.4463549 -0.4209974 0.01977248   0
##          NA         NA         NA   0
##  -0.4507341 -0.4435537 0.01765937   0
##  -0.4463146 -0.4209974 0.02058064   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.50 -0.49   -0.48  -0.48 -0.48   -0.48 -0.47 -0.46 0.00
##              AUC_b  0.43  0.49    0.53   0.56  0.56    0.59  0.62  0.73 0.04
##            AUC_oob  0.36  0.46    0.52   0.55  0.55    0.59  0.64  0.71 0.06
##    sum_sens_spec_b  0.85  1.00    1.08   1.13  1.13    1.18  1.26  1.44 0.08
##  sum_sens_spec_oob  0.76  0.92    1.01   1.08  1.08    1.14  1.24  1.36 0.10
##              acc_b  0.33  0.43    0.50   0.54  0.53    0.57  0.62  0.69 0.06
##            acc_oob  0.34  0.42    0.47   0.51  0.51    0.55  0.59  0.67 0.05
##      sensitivity_b  0.27  0.42    0.59   0.67  0.65    0.73  0.82  0.93 0.11
##    sensitivity_oob  0.09  0.35    0.52   0.63  0.62    0.72  0.85  1.00 0.15
##      specificity_b  0.13  0.28    0.40   0.49  0.48    0.55  0.64  0.82 0.11
##    specificity_oob  0.11  0.27    0.38   0.46  0.46    0.54  0.65  0.86 0.12
##     cohens_kappa_b -0.14  0.00    0.06   0.10  0.10    0.15  0.21  0.38 0.06
##   cohens_kappa_oob -0.23 -0.07    0.01   0.06  0.06    0.11  0.19  0.31 0.08
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
plot(cpK_MCI)

cpT <-
  cutpointr(
    filter(dados_lobos_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
    logAvgThickness,
    Diagnostic,
    ROI,
    pos_class = "AD",
    neg_class = "CTL",
    method = maximize_boot_metric,
    metric = sum_sens_spec,
    na.rm = TRUE,
    boot_runs = 1000,
    use_midpoints = TRUE
  )
summary(cpT)
## Method: maximize_boot_metric 
## Predictor: logAvgThickness 
## Outcome: Diagnostic 
## Direction: <= 
## Subgroups: F, P, T, O 
## Nr. of bootstraps: 1000 
## 
## Subgroup: F 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.8036 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##            0.3901          1.54 0.8249      0.6923      0.8477 18  8 23 128
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3573518 0.3705455 0.3912588 0.4023742 0.4034546 0.4148566 0.4402068
##       AD 0.3629861 0.3661257 0.3716468 0.3824120 0.3849235 0.3961625 0.4138817
##      MCI        NA        NA        NA        NA       NaN        NA        NA
##      CTL 0.3573518 0.3751557 0.3953965 0.4047286 0.4066454 0.4164345 0.4430437
##       Max.         SD NAs
##  0.4536341 0.02019155   0
##  0.4185876 0.01642300   0
##         NA         NA   0
##  0.4536341 0.01906939   0
## 
## Bootstrap summary: 
##           Variable  Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.38 0.38    0.39   0.39 0.39    0.39 0.40 0.41 0.00   0
##              AUC_b  0.63 0.72    0.77   0.81 0.80    0.84 0.88 0.94 0.05   0
##            AUC_oob  0.58 0.69    0.76   0.81 0.80    0.85 0.91 1.00 0.07   0
##    sum_sens_spec_b  1.12 1.35    1.45   1.51 1.51    1.58 1.68 1.83 0.10   0
##  sum_sens_spec_oob  0.97 1.20    1.36   1.46 1.45    1.55 1.69 1.86 0.15   0
##              acc_b  0.53 0.71    0.77   0.81 0.80    0.84 0.87 0.93 0.05   0
##            acc_oob  0.57 0.69    0.76   0.79 0.79    0.83 0.87 0.91 0.05   0
##      sensitivity_b  0.38 0.54    0.63   0.70 0.69    0.75 0.83 0.96 0.09   0
##    sensitivity_oob  0.12 0.33    0.50   0.67 0.64    0.75 0.89 1.00 0.17   0
##      specificity_b  0.48 0.72    0.79   0.83 0.82    0.86 0.90 0.96 0.06   0
##    specificity_oob  0.51 0.68    0.78   0.83 0.82    0.87 0.91 0.98 0.07   0
##     cohens_kappa_b  0.06 0.24    0.33   0.40 0.40    0.47 0.56 0.69 0.10   0
##   cohens_kappa_oob -0.02 0.16    0.28   0.35 0.35    0.43 0.53 0.67 0.11   0
## 
## Subgroup: P 
## -------------------------------------------------------------------------------- 
##    AUC   n n_pos n_neg
##  0.786 177    26   151
## 
##  optimal_cutpoint sum_sens_spec   acc sensitivity specificity tp fn fp tn
##            0.3602         1.527 0.678      0.8846      0.6424 23  3 54 97
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3208179 0.3363368 0.3520451 0.3643827 0.3645260 0.3762276 0.3982727
##       AD 0.3259872 0.3290103 0.3412247 0.3510509 0.3491394 0.3558869 0.3660903
##      MCI        NA        NA        NA        NA       NaN        NA        NA
##      CTL 0.3208179 0.3365847 0.3535749 0.3676422 0.3671754 0.3784593 0.3994138
##       Max.         SD NAs
##  0.4154907 0.01896322   0
##  0.3698453 0.01116366   0
##         NA         NA   0
##  0.4154907 0.01878994   0
## 
## Bootstrap summary: 
##           Variable  Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.35 0.36    0.36   0.36 0.36    0.36 0.36 0.37 0.00   0
##              AUC_b  0.64 0.72    0.76   0.79 0.79    0.81 0.84 0.87 0.04   0
##            AUC_oob  0.59 0.70    0.75   0.79 0.79    0.82 0.87 0.98 0.05   0
##    sum_sens_spec_b  1.20 1.39    1.48   1.53 1.53    1.58 1.65 1.73 0.08   0
##  sum_sens_spec_oob  0.97 1.25    1.42   1.51 1.49    1.58 1.67 1.76 0.13   0
##              acc_b  0.51 0.60    0.65   0.68 0.68    0.71 0.74 0.80 0.04   0
##            acc_oob  0.49 0.58    0.64   0.67 0.67    0.70 0.75 0.81 0.05   0
##      sensitivity_b  0.67 0.81    0.86   0.89 0.89    0.93 0.97 1.00 0.05   0
##    sensitivity_oob  0.20 0.60    0.79   0.88 0.85    1.00 1.00 1.00 0.14   0
##      specificity_b  0.46 0.55    0.61   0.64 0.64    0.67 0.72 0.78 0.05   0
##    specificity_oob  0.42 0.53    0.60   0.64 0.64    0.68 0.74 0.86 0.06   0
##     cohens_kappa_b  0.09 0.18    0.25   0.29 0.29    0.33 0.40 0.52 0.07   0
##   cohens_kappa_oob -0.02 0.13    0.21   0.27 0.27    0.32 0.40 0.55 0.08   0
## 
## Subgroup: T 
## -------------------------------------------------------------------------------- 
##   AUC   n n_pos n_neg
##  0.89 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##            0.4416        1.5362 0.7401      0.8077      0.7285 21  5 41 110
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3638006 0.4077181 0.4355525 0.4494434 0.4485649 0.4634193 0.4831833
##       AD 0.3638006 0.3863285 0.4003040 0.4174322 0.4180355 0.4369206 0.4461635
##      MCI        NA        NA        NA        NA       NaN        NA        NA
##      CTL 0.4050407 0.4239007 0.4409264 0.4543818 0.4538216 0.4649956 0.4844813
##       Max.         SD NAs
##  0.4986993 0.02311740   0
##  0.4514959 0.02305291   0
##         NA         NA   0
##  0.4986993 0.01868425   0
## 
## Bootstrap summary: 
##           Variable Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint 0.42 0.43    0.44   0.44 0.44    0.44 0.45 0.45 0.00   0
##              AUC_b 0.78 0.84    0.87   0.89 0.89    0.91 0.94 0.97 0.03   0
##            AUC_oob 0.75 0.82    0.86   0.89 0.89    0.92 0.95 1.00 0.04   0
##    sum_sens_spec_b 1.35 1.44    1.53   1.59 1.59    1.64 1.72 1.80 0.08   0
##  sum_sens_spec_oob 1.05 1.28    1.45   1.54 1.53    1.63 1.74 1.90 0.14   0
##              acc_b 0.60 0.69    0.74   0.77 0.78    0.81 0.88 0.94 0.06   0
##            acc_oob 0.54 0.66    0.72   0.76 0.76    0.80 0.86 0.92 0.06   0
##      sensitivity_b 0.54 0.68    0.76   0.82 0.82    0.88 0.94 1.00 0.08   0
##    sensitivity_oob 0.18 0.43    0.67   0.80 0.77    0.90 1.00 1.00 0.18   0
##      specificity_b 0.55 0.67    0.72   0.77 0.77    0.82 0.89 0.97 0.07   0
##    specificity_oob 0.47 0.62    0.70   0.75 0.76    0.82 0.91 1.00 0.09   0
##     cohens_kappa_b 0.16 0.26    0.34   0.39 0.40    0.46 0.57 0.78 0.09   0
##   cohens_kappa_oob 0.03 0.20    0.29   0.35 0.36    0.42 0.53 0.75 0.10   0
## 
## Subgroup: O 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6821 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##            0.3046        1.3016 0.6215      0.6923      0.6093 18  8 59 92
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.2562338 0.2713524 0.2930485 0.3071984 0.3079301 0.3235285 0.3414299
##       AD 0.2608827 0.2672465 0.2817425 0.2975927 0.2965027 0.3080038 0.3248181
##      MCI        NA        NA        NA        NA       NaN        NA        NA
##      CTL 0.2562338 0.2731140 0.2954743 0.3097651 0.3098977 0.3256400 0.3445245
##      Max.         SD NAs
##  0.373209 0.02179725   0
##  0.334749 0.01846444   0
##        NA         NA   0
##  0.373209 0.02177428   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.28  0.30    0.30   0.30 0.30    0.31 0.31 0.32 0.00   0
##              AUC_b  0.50  0.60    0.65   0.68 0.68    0.72 0.77 0.89 0.05   0
##            AUC_oob  0.41  0.56    0.63   0.68 0.68    0.73 0.80 0.95 0.08   0
##    sum_sens_spec_b  0.94  1.13    1.23   1.30 1.30    1.37 1.47 1.64 0.10   0
##  sum_sens_spec_oob  0.73  0.97    1.13   1.23 1.23    1.33 1.47 1.71 0.15   0
##              acc_b  0.38  0.50    0.58   0.63 0.63    0.68 0.73 0.83 0.07   0
##            acc_oob  0.34  0.47    0.56   0.61 0.61    0.66 0.72 0.82 0.08   0
##      sensitivity_b  0.29  0.50    0.62   0.70 0.69    0.76 0.85 0.96 0.11   0
##    sensitivity_oob  0.00  0.29    0.50   0.63 0.62    0.78 0.92 1.00 0.19   0
##      specificity_b  0.32  0.46    0.55   0.62 0.61    0.69 0.75 0.91 0.09   0
##    specificity_oob  0.25  0.43    0.53   0.61 0.60    0.68 0.77 0.95 0.11   0
##     cohens_kappa_b -0.03  0.06    0.12   0.17 0.17    0.21 0.29 0.41 0.07   0
##   cohens_kappa_oob -0.14 -0.02    0.07   0.13 0.12    0.18 0.26 0.43 0.08   0
plot(cpT)

cpT_MCI <-
  cutpointr(
    filter(dados_lobos_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
    logAvgThickness,
    Diagnostic,
    ROI,
    pos_class = "MCI",
    neg_class = "CTL",
    method = maximize_boot_metric,
    metric = sum_sens_spec,
    na.rm = TRUE,
    boot_runs = 1000,
    use_midpoints = TRUE
  )
summary(cpT_MCI)
## Method: maximize_boot_metric 
## Predictor: logAvgThickness 
## Outcome: Diagnostic 
## Direction: <= 
## Subgroups: F, P, T, O 
## Nr. of bootstraps: 1000 
## 
## Subgroup: F 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6053 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##            0.4037         1.099 0.5417      0.5692      0.5298 37 28 71 80
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3573518 0.3737044 0.3922699 0.4034387 0.4043320 0.4148788 0.4393955
##       AD        NA        NA        NA        NA       NaN        NA        NA
##      MCI 0.3591979 0.3664164 0.3892379 0.4007419 0.3989577 0.4097565 0.4264739
##      CTL 0.3573518 0.3751557 0.3953965 0.4047286 0.4066454 0.4164345 0.4430437
##       Max.         SD NAs
##  0.4536341 0.01891520   0
##         NA         NA   0
##  0.4314183 0.01754102   0
##  0.4536341 0.01906939   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.39  0.39    0.40   0.40 0.40    0.41 0.41 0.43 0.01   0
##              AUC_b  0.47  0.53    0.58   0.61 0.61    0.64 0.67 0.74 0.04   0
##            AUC_oob  0.44  0.51    0.57   0.60 0.60    0.64 0.69 0.76 0.05   0
##    sum_sens_spec_b  0.91  1.02    1.10   1.16 1.15    1.21 1.27 1.38 0.07   0
##  sum_sens_spec_oob  0.79  0.94    1.04   1.10 1.10    1.16 1.25 1.38 0.09   0
##              acc_b  0.31  0.45    0.52   0.56 0.56    0.60 0.67 0.76 0.07   0
##            acc_oob  0.33  0.43    0.49   0.53 0.54    0.58 0.64 0.73 0.07   0
##      sensitivity_b  0.27  0.41    0.52   0.62 0.62    0.72 0.82 0.97 0.13   0
##    sensitivity_oob  0.12  0.32    0.46   0.59 0.58    0.70 0.88 1.00 0.17   0
##      specificity_b  0.14  0.32    0.45   0.52 0.54    0.64 0.76 0.88 0.14   0
##    specificity_oob  0.07  0.29    0.42   0.50 0.51    0.62 0.75 0.93 0.15   0
##     cohens_kappa_b -0.06  0.02    0.09   0.13 0.13    0.17 0.24 0.33 0.07   0
##   cohens_kappa_oob -0.16 -0.05    0.03   0.08 0.08    0.13 0.21 0.37 0.08   0
## 
## Subgroup: P 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.5726 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##            0.3692        1.1426 0.5231      0.6923      0.4503 45 20 83 68
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3063354 0.3365749 0.3527768 0.3661864 0.3654310 0.3763397 0.3973651
##       AD        NA        NA        NA        NA       NaN        NA        NA
##      MCI 0.3063354 0.3322565 0.3525954 0.3640680 0.3613786 0.3725529 0.3892357
##      CTL 0.3208179 0.3365847 0.3535749 0.3676422 0.3671754 0.3784593 0.3994138
##       Max.         SD NAs
##  0.4154907 0.01883489   0
##         NA         NA   0
##  0.3971472 0.01845001   0
##  0.4154907 0.01878994   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.34  0.36    0.37   0.37 0.37    0.37 0.38 0.38 0.00   0
##              AUC_b  0.46  0.50    0.54   0.57 0.57    0.60 0.64 0.70 0.04   0
##            AUC_oob  0.40  0.49    0.54   0.57 0.57    0.61 0.66 0.73 0.05   0
##    sum_sens_spec_b  0.90  1.03    1.10   1.15 1.15    1.19 1.26 1.38 0.07   0
##  sum_sens_spec_oob  0.77  0.94    1.04   1.11 1.10    1.17 1.25 1.40 0.09   0
##              acc_b  0.33  0.44    0.49   0.52 0.52    0.55 0.60 0.70 0.05   0
##            acc_oob  0.34  0.42    0.47   0.50 0.50    0.54 0.59 0.71 0.05   0
##      sensitivity_b  0.13  0.47    0.65   0.73 0.71    0.80 0.86 0.94 0.12   0
##    sensitivity_oob  0.09  0.39    0.59   0.70 0.67    0.78 0.88 1.00 0.15   0
##      specificity_b  0.14  0.27    0.37   0.43 0.44    0.50 0.62 0.89 0.10   0
##    specificity_oob  0.13  0.26    0.35   0.42 0.43    0.50 0.63 0.94 0.12   0
##     cohens_kappa_b -0.09  0.02    0.08   0.11 0.11    0.15 0.21 0.32 0.06   0
##   cohens_kappa_oob -0.20 -0.05    0.04   0.08 0.08    0.13 0.19 0.32 0.07   0
## 
## Subgroup: T 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6889 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##            0.4404        1.3308 0.7037      0.5692      0.7616 37 28 36 115
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.3828083 0.4162291 0.4372204 0.4501150 0.4496988 0.4628756 0.4820372
##       AD        NA        NA        NA        NA       NaN        NA        NA
##      MCI 0.3828083 0.4059276 0.4282558 0.4385578 0.4401212 0.4552104 0.4709293
##      CTL 0.4050407 0.4239007 0.4409264 0.4543818 0.4538216 0.4649956 0.4844813
##       Max.         SD NAs
##  0.4986993 0.02008186   0
##         NA         NA   0
##  0.4824209 0.02008228   0
##  0.4986993 0.01868425   0
## 
## Bootstrap summary: 
##           Variable  Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.43 0.44    0.44   0.44 0.44    0.44 0.45 0.46 0.00   0
##              AUC_b  0.51 0.62    0.66   0.69 0.69    0.72 0.75 0.81 0.04   0
##            AUC_oob  0.51 0.61    0.66   0.69 0.69    0.72 0.77 0.85 0.05   0
##    sum_sens_spec_b  0.94 1.18    1.28   1.34 1.33    1.39 1.46 1.58 0.09   0
##  sum_sens_spec_oob  1.01 1.14    1.24   1.31 1.30    1.37 1.45 1.59 0.09   0
##              acc_b  0.47 0.60    0.67   0.70 0.70    0.73 0.76 0.81 0.05   0
##            acc_oob  0.47 0.58    0.66   0.69 0.68    0.72 0.76 0.81 0.05   0
##      sensitivity_b  0.36 0.49    0.56   0.59 0.59    0.63 0.69 0.78 0.06   0
##    sensitivity_oob  0.22 0.41    0.52   0.58 0.57    0.64 0.74 0.87 0.10   0
##      specificity_b  0.47 0.61    0.71   0.75 0.74    0.79 0.83 0.88 0.07   0
##    specificity_oob  0.34 0.56    0.69   0.74 0.73    0.79 0.85 0.90 0.08   0
##     cohens_kappa_b -0.06 0.16    0.26   0.32 0.32    0.38 0.45 0.54 0.09   0
##   cohens_kappa_oob  0.01 0.13    0.23   0.29 0.29    0.35 0.43 0.55 0.09   0
## 
## Subgroup: O 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.5585 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn  fp tn
##            0.3176        1.1157 0.4676      0.7846      0.3311 51 14 101 50
## 
## Predictor summary: 
##     Data      Min.        5%   1st Qu.    Median      Mean   3rd Qu.       95%
##  Overall 0.2562338 0.2722319 0.2952212 0.3075814 0.3085263 0.3222856 0.3406063
##       AD        NA        NA        NA        NA       NaN        NA        NA
##      MCI 0.2648261 0.2725975 0.2949457 0.3050174 0.3053404 0.3170994 0.3336167
##      CTL 0.2562338 0.2731140 0.2954743 0.3097651 0.3098977 0.3256400 0.3445245
##       Max.         SD NAs
##  0.3732090 0.02072181   0
##         NA         NA   0
##  0.3429703 0.01779132   0
##  0.3732090 0.02177428   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint  0.29  0.30    0.31   0.32 0.31    0.32 0.32 0.33 0.01   0
##              AUC_b  0.44  0.49    0.53   0.56 0.56    0.59 0.63 0.72 0.04   0
##            AUC_oob  0.33  0.47    0.52   0.56 0.56    0.60 0.64 0.75 0.05   0
##    sum_sens_spec_b  0.86  1.00    1.07   1.12 1.12    1.17 1.23 1.41 0.07   0
##  sum_sens_spec_oob  0.71  0.91    1.01   1.08 1.07    1.14 1.22 1.32 0.10   0
##              acc_b  0.35  0.42    0.46   0.50 0.50    0.53 0.60 0.72 0.06   0
##            acc_oob  0.28  0.40    0.44   0.48 0.48    0.52 0.57 0.67 0.05   0
##      sensitivity_b  0.23  0.51    0.62   0.70 0.71    0.80 0.89 0.96 0.12   0
##    sensitivity_oob  0.16  0.41    0.57   0.68 0.67    0.79 0.90 1.00 0.16   0
##      specificity_b  0.11  0.26    0.32   0.39 0.41    0.50 0.63 0.84 0.12   0
##    specificity_oob  0.12  0.23    0.31   0.39 0.40    0.48 0.60 0.79 0.11   0
##     cohens_kappa_b -0.11  0.00    0.05   0.09 0.09    0.13 0.18 0.39 0.06   0
##   cohens_kappa_oob -0.20 -0.07    0.01   0.06 0.05    0.10 0.17 0.28 0.07   0
plot(cpT_MCI)

dat_text <- data.frame(label = c("4 cylinders", "6 cylinders", "8 cylinders"),
                       cyl   = c(4, 6, 8))

cutpoint_a_lobes <-
  ggplot(dados_lobos_v1,
         aes(
           x = K_corrected,
           color = Diagnostic,
           fill = Diagnostic,
           alpha = 0.4
         )) +
  geom_density() +
  geom_vline(data = cpK,
             aes(
               xintercept = optimal_cutpoint,
               linetype = "dashed",
               group = subgroup
             )) +
  geom_vline(data = cpK_MCI,
             aes(
               xintercept = optimal_cutpoint,
               linetype = "dotted",
               group = subgroup
             )) +
  theme_pubr() +
  guides(alpha = "none", linetype = "none") +
  theme(
    axis.title = element_text(size = 11),
    axis.text = element_text(size = 10),
    text = element_text(size = 10)
  ) +
  facet_grid(subgroup ~ .)
# cutpoint_a


cutpoint_b_lobes <-
  ggplot(
    dados_lobos_v1,
    aes(
      x = logAvgThickness,
      color = Diagnostic,
      fill = Diagnostic,
      alpha = 0.4
    )
  ) +
  geom_density() +
  geom_vline(data = cpT,
             aes(
               xintercept = optimal_cutpoint,
               linetype = "dashed",
               group = subgroup
             )) +
  geom_vline(data = cpT_MCI,
             aes(
               xintercept = optimal_cutpoint,
               linetype = "dotted",
               group = subgroup
             )) +
  theme_pubr() +
  guides(alpha = "none", linetype = "none") +
  theme(
    axis.title = element_text(size = 11),
    axis.text = element_text(size = 10),
    text = element_text(size = 10)
  ) + facet_grid(subgroup ~ .)
# cutpoint_b

fig_cutpoint_lobes <-
  ggarrange(
    cutpoint_a_lobes,
    cutpoint_b_lobes,
    labels = c("A", "B"),
    ncol = 1,
    font.label = list(size = 11),
    common.legend = TRUE,
    legend = "top"
  )

fig_cutpoint_lobes

11 Table 1

11.0.1 Girifcacao e envelhecimento

11.0.1.1 Reduzindo o efeito da idade

## 
## Call:
## lm(formula = 1/2 * logAvgThickness_age_decay + logTotalArea_age_decay ~ 
##     logExposedArea_age_decay, data = dados_hemi_v1, na.action = na.omit)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.032355 -0.008226  0.000773  0.008828  0.028420 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.41298    0.13258   3.115  0.00206 ** 
## logExposedArea_age_decay  1.05030    0.02873  36.554  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01196 on 244 degrees of freedom
## Multiple R-squared:  0.8456, Adjusted R-squared:  0.845 
## F-statistic:  1336 on 1 and 244 DF,  p-value: < 2.2e-16
Diagnostic term estimate std.error statistic p.value conf.low conf.high
AD (Intercept) 0.42 0.37 1.14 0.26 -0.34 1.18
AD logExposedArea_age_decay 1.05 0.08 13.07 0.00 0.88 1.21
MCI (Intercept) 0.61 0.20 3.05 0.00 0.21 1.01
MCI logExposedArea_age_decay 1.01 0.04 23.28 0.00 0.92 1.09
CTL (Intercept) 0.18 0.18 1.00 0.32 -0.17 0.53
CTL logExposedArea_age_decay 1.10 0.04 28.77 0.00 1.03 1.18 
## 
##  Kruskal-Wallis rank sum test
## 
## data:  estimate by Diagnostic
## Kruskal-Wallis chi-squared = 2, df = 2, p-value = 0.3679

11.0.2 Diferenca entre diagnosticos

11.0.2.1 Cut point

11.0.2.1.1 Lobes deaged
cpK <-
    cutpointr(
        filter(dados_lobos_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
        K_age_decay,
        Diagnostic,
        ROI,
        pos_class = "AD",
        neg_class = "CTL",
        method = maximize_boot_metric,
        metric = sum_sens_spec,
        na.rm = TRUE,
        boot_runs = 1000,
    use_midpoints = TRUE)
summary(cpK)
## Method: maximize_boot_metric 
## Predictor: K_age_decay 
## Outcome: Diagnostic 
## Direction: <= 
## Subgroups: F, P, T, O 
## Nr. of bootstraps: 1000 
## 
## Subgroup: F 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6994 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.4644        1.2443 0.7627      0.4231      0.8212 11 15 27 124
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.4924472 -0.4777872 -0.4618779 -0.4493759 -0.4504447 -0.4382565
##       AD -0.4924472 -0.4858975 -0.4771550 -0.4608831 -0.4613222 -0.4447041
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.4889670 -0.4736635 -0.4585019 -0.4487091 -0.4485717 -0.4360336
##         95%       Max.         SD NAs
##  -0.4272281 -0.4191747 0.01623795   0
##  -0.4359569 -0.4332498 0.01813598   0
##          NA         NA         NA   0
##  -0.4264298 -0.4191747 0.01518272   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.48 -0.47   -0.47  -0.46 -0.46   -0.46 -0.45 -0.43 0.01
##              AUC_b  0.45  0.60    0.66   0.70  0.70    0.74  0.79  0.89 0.06
##            AUC_oob  0.34  0.57    0.65   0.70  0.70    0.76  0.84  0.98 0.08
##    sum_sens_spec_b  0.79  1.10    1.21   1.29  1.29    1.36  1.47  1.61 0.12
##  sum_sens_spec_oob  0.74  1.00    1.16   1.25  1.25    1.34  1.48  1.65 0.14
##              acc_b  0.32  0.54    0.68   0.74  0.73    0.79  0.86  0.94 0.09
##            acc_oob  0.38  0.55    0.67   0.73  0.72    0.78  0.84  0.94 0.09
##      sensitivity_b  0.28  0.38    0.45   0.52  0.52    0.59  0.70  0.93 0.10
##    sensitivity_oob  0.00  0.17    0.33   0.50  0.49    0.62  0.83  1.00 0.20
##      specificity_b  0.21  0.53    0.71   0.78  0.76    0.84  0.93  0.98 0.11
##    specificity_oob  0.25  0.52    0.69   0.78  0.76    0.84  0.93  0.98 0.12
##     cohens_kappa_b -0.09  0.05    0.14   0.22  0.22    0.29  0.43  0.63 0.11
##   cohens_kappa_oob -0.16  0.00    0.11   0.18  0.18    0.25  0.37  0.56 0.11
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: P 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.7858 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.3369        1.4845 0.7232      0.7692      0.7152 20  6 43 108
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.3887231 -0.3609292 -0.3417547 -0.3303306 -0.3324749 -0.3209917
##       AD -0.3673445 -0.3645357 -0.3577182 -0.3451577 -0.3459457 -0.3386622
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.3887231 -0.3595692 -0.3382488 -0.3285399 -0.3301554 -0.3194001
##         95%       Max.         SD NAs
##  -0.3093390 -0.2967149 0.01692941   0
##  -0.3260820 -0.3201238 0.01326828   0
##          NA         NA         NA   0
##  -0.3082498 -0.2967149 0.01643388   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.35 -0.34   -0.34  -0.34 -0.34   -0.34 -0.33 -0.33 0.00
##              AUC_b  0.53  0.71    0.76   0.79  0.79    0.82  0.85  0.93 0.04
##            AUC_oob  0.62  0.68    0.75   0.78  0.78    0.82  0.88  0.94 0.06
##    sum_sens_spec_b  1.06  1.29    1.40   1.48  1.47    1.54  1.65  1.76 0.11
##  sum_sens_spec_oob  0.93  1.16    1.34   1.44  1.43    1.53  1.66  1.77 0.15
##              acc_b  0.44  0.63    0.69   0.73  0.73    0.77  0.81  0.91 0.06
##            acc_oob  0.46  0.62    0.68   0.71  0.72    0.75  0.81  0.88 0.06
##      sensitivity_b  0.47  0.59    0.70   0.75  0.75    0.81  0.88  0.96 0.09
##    sensitivity_oob  0.09  0.38    0.60   0.73  0.71    0.83  1.00  1.00 0.17
##      specificity_b  0.41  0.61    0.68   0.73  0.72    0.77  0.83  0.95 0.07
##    specificity_oob  0.37  0.58    0.67   0.72  0.72    0.77  0.84  0.93 0.08
##     cohens_kappa_b  0.02  0.17    0.24   0.31  0.31    0.37  0.46  0.64 0.09
##   cohens_kappa_oob -0.07  0.11    0.21   0.27  0.27    0.34  0.42  0.56 0.10
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: T 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.7504 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.3434        1.3372 0.7062      0.6154      0.7219 16 10 42 109
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.3862226 -0.3693890 -0.3449413 -0.3362110 -0.3372773 -0.3269770
##       AD -0.3862226 -0.3793668 -0.3651671 -0.3476139 -0.3513326 -0.3366410
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.3723159 -0.3605893 -0.3441472 -0.3347947 -0.3348572 -0.3258725
##         95%       Max.         SD NAs
##  -0.3115348 -0.2980570 0.01646089   0
##  -0.3310313 -0.3219632 0.01803107   0
##          NA         NA         NA   0
##  -0.3109501 -0.2980570 0.01495334   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.36 -0.35   -0.35  -0.34 -0.34   -0.34 -0.33 -0.33 0.01
##              AUC_b  0.56  0.66    0.71   0.75  0.75    0.78  0.83  0.90 0.05
##            AUC_oob  0.35  0.64    0.71   0.76  0.75    0.81  0.87  1.00 0.07
##    sum_sens_spec_b  1.00  1.16    1.27   1.34  1.34    1.42  1.51  1.68 0.11
##  sum_sens_spec_oob  0.75  1.04    1.19   1.28  1.28    1.37  1.52  1.75 0.14
##              acc_b  0.43  0.53    0.63   0.69  0.69    0.77  0.83  0.94 0.09
##            acc_oob  0.37  0.50    0.61   0.68  0.68    0.76  0.82  0.92 0.10
##      sensitivity_b  0.27  0.45    0.56   0.64  0.64    0.72  0.82  0.96 0.11
##    sensitivity_oob  0.00  0.23    0.44   0.60  0.59    0.75  0.90  1.00 0.21
##      specificity_b  0.38  0.49    0.62   0.70  0.70    0.80  0.88  0.99 0.12
##    specificity_oob  0.25  0.45    0.60   0.70  0.69    0.81  0.89  0.98 0.14
##     cohens_kappa_b  0.00  0.08    0.15   0.22  0.23    0.30  0.42  0.71 0.10
##   cohens_kappa_oob -0.16  0.03    0.11   0.18  0.18    0.25  0.37  0.47 0.10
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: O 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6694 177    26   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.3556         1.243 0.7345      0.4615      0.7815 12 14 33 118
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.4071304 -0.3816251 -0.3556343 -0.3413318 -0.3412010 -0.3271589
##       AD -0.4071304 -0.3888987 -0.3758057 -0.3538700 -0.3547200 -0.3347385
##      MCI         NA         NA         NA         NA        NaN         NA
##      CTL -0.3914546 -0.3758727 -0.3531679 -0.3392267 -0.3388732 -0.3262021
##         95%       Max.         SD NAs
##  -0.2999387 -0.2784499 0.02333062   0
##  -0.3130321 -0.3074425 0.02669722   0
##          NA         NA         NA   0
##  -0.2976979 -0.2784499 0.02197141   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.38 -0.37   -0.36  -0.35 -0.35   -0.35 -0.35 -0.34 0.01
##              AUC_b  0.43  0.56    0.62   0.67  0.67    0.72  0.77  0.87 0.07
##            AUC_oob  0.35  0.52    0.61   0.67  0.67    0.73  0.83  0.96 0.09
##    sum_sens_spec_b  0.92  1.12    1.22   1.29  1.29    1.36  1.46  1.57 0.11
##  sum_sens_spec_oob  0.78  0.98    1.13   1.23  1.23    1.33  1.48  1.68 0.15
##              acc_b  0.50  0.62    0.69   0.73  0.73    0.78  0.85  0.90 0.07
##            acc_oob  0.44  0.58    0.66   0.72  0.71    0.78  0.83  0.90 0.08
##      sensitivity_b  0.23  0.32    0.43   0.52  0.52    0.62  0.73  0.91 0.13
##    sensitivity_oob  0.00  0.17    0.33   0.50  0.48    0.60  0.80  1.00 0.19
##      specificity_b  0.47  0.62    0.71   0.77  0.77    0.83  0.92  0.97 0.09
##    specificity_oob  0.44  0.58    0.68   0.75  0.76    0.84  0.93  0.98 0.11
##     cohens_kappa_b -0.04  0.08    0.15   0.21  0.22    0.28  0.39  0.51 0.09
##   cohens_kappa_oob -0.16 -0.01    0.10   0.17  0.17    0.25  0.36  0.50 0.11
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
plot(cpK)

cpK_MCI <-
    cutpointr(
        filter(dados_lobos_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
        K_age_decay,
        Diagnostic,
        ROI,
        pos_class = "MCI",
        neg_class = "CTL",
        method = maximize_boot_metric,
        metric = sum_sens_spec,
        na.rm = TRUE,
        boot_runs = 1000,
    use_midpoints = TRUE)
summary(cpK_MCI)
## Method: maximize_boot_metric 
## Predictor: K_age_decay 
## Outcome: Diagnostic 
## Direction: <= 
## Subgroups: F, P, T, O 
## Nr. of bootstraps: 1000 
## 
## Subgroup: F 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.5416 216    65   151
## 
##  optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
##           -0.4488        0.9869 0.5      0.4769      0.5099 31 34 74 77
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.4889670 -0.4757776 -0.4597265 -0.4485104 -0.4492211 -0.4378026
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.4830813 -0.4761680 -0.4609377 -0.4482128 -0.4507296 -0.4401716
##      CTL -0.4889670 -0.4736635 -0.4585019 -0.4487091 -0.4485717 -0.4360336
##         95%       Max.         SD NAs
##  -0.4267766 -0.4173079 0.01535924   0
##          NA         NA         NA   0
##  -0.4272361 -0.4173079 0.01577727   0
##  -0.4264298 -0.4191747 0.01518272   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.47 -0.46   -0.45  -0.45 -0.45   -0.45 -0.44 -0.43 0.01
##              AUC_b  0.39  0.47    0.51   0.54  0.54    0.57  0.61  0.69 0.04
##            AUC_oob  0.32  0.45    0.51   0.54  0.54    0.58  0.64  0.75 0.06
##    sum_sens_spec_b  0.83  0.94    1.02   1.07  1.07    1.12  1.20  1.33 0.08
##  sum_sens_spec_oob  0.66  0.87    0.96   1.03  1.03    1.09  1.19  1.38 0.10
##              acc_b  0.32  0.44    0.50   0.54  0.54    0.60  0.65  0.72 0.07
##            acc_oob  0.31  0.41    0.47   0.53  0.53    0.58  0.66  0.75 0.08
##      sensitivity_b  0.14  0.25    0.41   0.50  0.51    0.60  0.76  0.91 0.15
##    sensitivity_oob  0.00  0.20    0.37   0.48  0.48    0.60  0.75  0.96 0.17
##      specificity_b  0.12  0.33    0.45   0.56  0.56    0.67  0.80  0.94 0.14
##    specificity_oob  0.12  0.29    0.44   0.54  0.55    0.66  0.81  0.96 0.16
##     cohens_kappa_b -0.14 -0.05    0.01   0.06  0.06    0.10  0.18  0.30 0.07
##   cohens_kappa_oob -0.31 -0.11   -0.03   0.02  0.02    0.08  0.17  0.36 0.09
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: P 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.4746 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.3423        1.1003 0.6528      0.2923      0.8079 19 46 29 122
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.3887231 -0.3615902 -0.3400921 -0.3280799 -0.3298689 -0.3182982
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.3733775 -0.3624233 -0.3442249 -0.3234164 -0.3292033 -0.3144146
##      CTL -0.3887231 -0.3595692 -0.3382488 -0.3285399 -0.3301554 -0.3194001
##         95%       Max.         SD NAs
##  -0.3064748 -0.2866583 0.01754076   0
##          NA         NA         NA   0
##  -0.3037192 -0.2866583 0.01999951   0
##  -0.3082498 -0.2967149 0.01643388   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.36 -0.35   -0.35  -0.34 -0.34   -0.34 -0.33 -0.32 0.00
##              AUC_b  0.34  0.41    0.44   0.48  0.48    0.51  0.56  0.63 0.05
##            AUC_oob  0.28  0.37    0.43   0.47  0.47    0.51  0.57  0.69 0.06
##    sum_sens_spec_b  0.69  0.96    1.05   1.10  1.10    1.15  1.21  1.32 0.08
##  sum_sens_spec_oob  0.76  0.92    1.00   1.05  1.06    1.11  1.20  1.34 0.08
##              acc_b  0.33  0.56    0.62   0.65  0.65    0.68  0.72  0.77 0.05
##            acc_oob  0.29  0.55    0.60   0.64  0.63    0.67  0.71  0.78 0.05
##      sensitivity_b  0.10  0.17    0.25   0.30  0.30    0.35  0.42  0.67 0.07
##    sensitivity_oob  0.00  0.11    0.20   0.26  0.27    0.33  0.44  0.68 0.10
##      specificity_b  0.27  0.65    0.76   0.81  0.80    0.85  0.90  0.95 0.09
##    specificity_oob  0.11  0.65    0.75   0.80  0.79    0.85  0.91  0.98 0.09
##     cohens_kappa_b -0.24 -0.04    0.06   0.11  0.11    0.16  0.23  0.35 0.08
##   cohens_kappa_oob -0.22 -0.08    0.00   0.06  0.06    0.12  0.21  0.32 0.09
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: T 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.6057 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##           -0.3468        1.1838 0.6806      0.3692      0.8146 24 41 28 123
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.3801095 -0.3621463 -0.3458489 -0.3363093 -0.3368114 -0.3262343
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.3801095 -0.3674622 -0.3556756 -0.3423442 -0.3413513 -0.3265369
##      CTL -0.3723159 -0.3605893 -0.3441472 -0.3347947 -0.3348572 -0.3258725
##         95%       Max.         SD NAs
##  -0.3112199 -0.2980570 0.01598388   0
##          NA         NA         NA   0
##  -0.3135545 -0.3066956 0.01744295   0
##  -0.3109501 -0.2980570 0.01495334   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.35 -0.35   -0.35  -0.35 -0.35   -0.34 -0.34 -0.33 0.00
##              AUC_b  0.46  0.53    0.58   0.61  0.61    0.64  0.68  0.77 0.04
##            AUC_oob  0.42  0.51    0.56   0.60  0.60    0.64  0.70  0.79 0.06
##    sum_sens_spec_b  0.96  1.10    1.17   1.22  1.22    1.27  1.33  1.42 0.07
##  sum_sens_spec_oob  0.87  1.02    1.11   1.17  1.17    1.24  1.32  1.44 0.09
##              acc_b  0.40  0.59    0.65   0.68  0.68    0.71  0.75  0.81 0.05
##            acc_oob  0.47  0.56    0.62   0.66  0.66    0.70  0.74  0.86 0.06
##      sensitivity_b  0.22  0.32    0.38   0.43  0.44    0.49  0.58  0.78 0.08
##    sensitivity_oob  0.10  0.24    0.33   0.40  0.40    0.46  0.59  0.83 0.11
##      specificity_b  0.28  0.63    0.73   0.80  0.78    0.84  0.88  0.94 0.08
##    specificity_oob  0.33  0.59    0.71   0.79  0.77    0.84  0.90  0.99 0.10
##     cohens_kappa_b -0.03  0.09    0.17   0.22  0.22    0.27  0.35  0.45 0.08
##   cohens_kappa_oob -0.13  0.02    0.11   0.17  0.17    0.24  0.33  0.45 0.10
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
## 
## Subgroup: O 
## -------------------------------------------------------------------------------- 
##     AUC   n n_pos n_neg
##  0.5627 216    65   151
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp tn
##           -0.3348        1.1159 0.4861      0.7385      0.3775 48 17 94 57
## 
## Predictor summary: 
##     Data       Min.         5%    1st Qu.     Median       Mean    3rd Qu.
##  Overall -0.3914546 -0.3737629 -0.3531416 -0.3404060 -0.3401636 -0.3291942
##       AD         NA         NA         NA         NA        NaN         NA
##      MCI -0.3738751 -0.3664919 -0.3525155 -0.3446323 -0.3431611 -0.3340610
##      CTL -0.3914546 -0.3758727 -0.3531679 -0.3392267 -0.3388732 -0.3262021
##         95%       Max.         SD NAs
##  -0.3074186 -0.2784499 0.02028191   0
##          NA         NA         NA   0
##  -0.3133603 -0.3085133 0.01540832   0
##  -0.2976979 -0.2784499 0.02197141   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint -0.35 -0.34   -0.34  -0.34 -0.34   -0.33 -0.33 -0.31 0.01
##              AUC_b  0.42  0.50    0.53   0.56  0.56    0.59  0.63  0.71 0.04
##            AUC_oob  0.35  0.47    0.53   0.56  0.56    0.60  0.65  0.76 0.06
##    sum_sens_spec_b  0.92  1.03    1.09   1.14  1.14    1.19  1.26  1.39 0.07
##  sum_sens_spec_oob  0.78  0.92    1.03   1.09  1.09    1.16  1.24  1.39 0.09
##              acc_b  0.34  0.41    0.46   0.51  0.51    0.56  0.61  0.70 0.06
##            acc_oob  0.33  0.40    0.45   0.49  0.49    0.53  0.58  0.64 0.06
##      sensitivity_b  0.42  0.57    0.65   0.72  0.72    0.78  0.87  0.92 0.09
##    sensitivity_oob  0.19  0.43    0.59   0.70  0.69    0.80  0.91  1.00 0.15
##      specificity_b  0.09  0.25    0.33   0.42  0.42    0.51  0.61  0.78 0.12
##    specificity_oob  0.10  0.22    0.32   0.40  0.41    0.49  0.60  0.71 0.12
##     cohens_kappa_b -0.06  0.02    0.07   0.11  0.11    0.15  0.22  0.33 0.06
##   cohens_kappa_oob -0.18 -0.06    0.02   0.07  0.07    0.12  0.18  0.27 0.07
##  NAs
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
##    0
plot(cpK_MCI)

11.0.2.2 LM

Diagnostic term estimate std.error statistic p.value conf.low conf.high
AD (Intercept) 0.20 0.39 0.50 0.62 -0.62 1.01
AD logExposedArea 1.09 0.09 12.60 0.00 0.91 1.27
MCI (Intercept) 0.53 0.21 2.50 0.02 0.11 0.95
MCI logExposedArea 1.02 0.05 22.08 0.00 0.93 1.11
CTL (Intercept) -0.23 0.18 -1.27 0.21 -0.60 0.13
CTL logExposedArea 1.19 0.04 29.52 0.00 1.11 1.27 

##                  Df Sum Sq Mean Sq  F value Pr(>F)    
## Diagnostic        2  0.058  0.0292   81.596 <2e-16 ***
## ROI               4  6.222  1.5556 4347.166 <2e-16 ***
## Diagnostic:ROI    8  0.003  0.0004    1.013  0.424    
## Residuals      1207  0.432  0.0004                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(dados_hemi_v1, aes(x = Diagnostic, y = S, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot()  + theme_pubr()

ggplot(dados_hemi_v1, aes(x= S, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_density() + theme_pubr()

ggbetweenstats(
    data = dados_hemi_v1,
    x = Diagnostic,
    y = S, outlier.tagging = TRUE,
    plot.type = "box"
) 

aov_diag <- aov(S ~ Diagnostic, data = dados_hemi_v1)
summary(aov_diag)
##              Df Sum Sq Mean Sq F value Pr(>F)   
## Diagnostic    2 0.1539 0.07697   6.275 0.0022 **
## Residuals   243 2.9810 0.01227                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov_diag_K_diag_TK <- TukeyHSD(aov_diag)
aov_diag_K_diag_TK
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = S ~ Diagnostic, data = dados_hemi_v1)
## 
## $Diagnostic
##                diff         lwr          upr     p adj
## MCI-AD  -0.04832085 -0.10879738  0.012155678 0.1454395
## CTL-AD  -0.07844671 -0.13382516 -0.023068259 0.0027747
## CTL-MCI -0.03012586 -0.06855234  0.008300619 0.1561058
ggplot(dados_hemi_v1, aes(x = Diagnostic, y = I, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot()  + theme_pubr()

ggplot(dados_hemi_v1, aes(x= I, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_density() + theme_pubr()

ggbetweenstats(
    data = dados_hemi_v1,
    x = Diagnostic,
    y = I, outlier.tagging = TRUE,
    plot.type = "box"
) 

aov_diag <- aov(I ~ Diagnostic, data = dados_hemi_v1)
summary(aov_diag)
##              Df Sum Sq Mean Sq F value  Pr(>F)    
## Diagnostic    2 0.1083 0.05415   11.21 2.2e-05 ***
## Residuals   243 1.1734 0.00483                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov_diag_K_diag_TK <- TukeyHSD(aov_diag)
aov_diag_K_diag_TK
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = I ~ Diagnostic, data = dados_hemi_v1)
## 
## $Diagnostic
##               diff         lwr        upr     p adj
## MCI-AD  0.04158669 0.003644613 0.07952877 0.0277853
## CTL-AD  0.06616956 0.031425940 0.10091318 0.0000325
## CTL-MCI 0.02458287 0.000474665 0.04869107 0.0445049

Is it easier to diff diag when younger?

ggplot(filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"), aes(x = Age_interval, y = logAvgThickness, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot()  + theme_pubr()

aov_diag2 <- aov(logAvgThickness ~ Diagnostic*Age_interval, data = filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"))
summary(aov_diag2)
##                          Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic                2 0.00951 0.004754  24.206 3.90e-10 ***
## Age_interval              5 0.01008 0.002016  10.266 9.08e-09 ***
## Diagnostic:Age_interval   7 0.00581 0.000830   4.224 0.000228 ***
## Residuals               199 0.03909 0.000196                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov_diag_2_diag_TK <- TukeyHSD(aov_diag2)
aov_diag_2_diag_TK$`Diagnostic:Age_interval`
##                                  diff          lwr           upr        p adj
## MCI:[60,65)-AD:[60,65)   0.0247295988 -0.024817696  0.0742768933 9.504734e-01
## CTL:[60,65)-AD:[60,65)   0.0440618281  0.007948342  0.0801753144 3.252613e-03
## AD:[65,70)-AD:[60,65)              NA           NA            NA           NA
## MCI:[65,70)-AD:[60,65)   0.0392822521  0.002536995  0.0760275091 2.283589e-02
## CTL:[65,70)-AD:[60,65)   0.0430458757  0.007050601  0.0790411506 4.534547e-03
## AD:[70,75)-AD:[60,65)    0.0202524211 -0.020202775  0.0607076177 9.491546e-01
## MCI:[70,75)-AD:[60,65)   0.0308792707 -0.005478504  0.0672370450 2.092742e-01
## CTL:[70,75)-AD:[60,65)   0.0228814145 -0.013169538  0.0589323666 7.217380e-01
## AD:[75,80)-AD:[60,65)    0.0179841586 -0.020395011  0.0563633279 9.721875e-01
## MCI:[75,80)-AD:[60,65)   0.0226882735 -0.015154098  0.0605306448 8.015808e-01
## CTL:[75,80)-AD:[60,65)   0.0332570904 -0.003673282  0.0701874633 1.347703e-01
## AD:[80,85)-AD:[60,65)    0.0183866342 -0.022068562  0.0588418308 9.793185e-01
## MCI:[80,85)-AD:[60,65)   0.0117412129 -0.028713984  0.0521964094 9.999045e-01
## CTL:[80,85)-AD:[60,65)   0.0383324981 -0.011214796  0.0878797926 3.636968e-01
## AD:[85,90)-AD:[60,65)   -0.0052373330 -0.054784627  0.0443099615 1.000000e+00
## MCI:[85,90)-AD:[60,65)             NA           NA            NA           NA
## CTL:[85,90)-AD:[60,65)             NA           NA            NA           NA
## CTL:[60,65)-MCI:[60,65)  0.0193322293 -0.016781257  0.0554457156 9.113170e-01
## AD:[65,70)-MCI:[60,65)             NA           NA            NA           NA
## MCI:[65,70)-MCI:[60,65)  0.0145526533 -0.022192604  0.0512979103 9.952006e-01
## CTL:[65,70)-MCI:[60,65)  0.0183162769 -0.017678998  0.0543115518 9.414707e-01
## AD:[70,75)-MCI:[60,65)  -0.0044771777 -0.044932374  0.0359780189 1.000000e+00
## MCI:[70,75)-MCI:[60,65)  0.0061496719 -0.030208102  0.0425074462 1.000000e+00
## CTL:[70,75)-MCI:[60,65) -0.0018481843 -0.037899136  0.0342027678 1.000000e+00
## AD:[75,80)-MCI:[60,65)  -0.0067454402 -0.045124609  0.0316337291 9.999999e-01
## MCI:[75,80)-MCI:[60,65) -0.0020413253 -0.039883697  0.0358010460 1.000000e+00
## CTL:[75,80)-MCI:[60,65)  0.0085274916 -0.028402881  0.0454578645 9.999964e-01
## AD:[80,85)-MCI:[60,65)  -0.0063429646 -0.046798161  0.0341122320 1.000000e+00
## MCI:[80,85)-MCI:[60,65) -0.0129883859 -0.053443582  0.0274668106 9.996311e-01
## CTL:[80,85)-MCI:[60,65)  0.0136028994 -0.035944395  0.0631501939 9.999558e-01
## AD:[85,90)-MCI:[60,65)  -0.0299669318 -0.079514226  0.0195803627 7.903899e-01
## MCI:[85,90)-MCI:[60,65)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[60,65)            NA           NA            NA           NA
## AD:[65,70)-CTL:[60,65)             NA           NA            NA           NA
## MCI:[65,70)-CTL:[60,65) -0.0047795760 -0.018902728  0.0093435759 9.992738e-01
## CTL:[65,70)-CTL:[60,65) -0.0010159524 -0.013053781  0.0110218764 1.000000e+00
## AD:[70,75)-CTL:[60,65)  -0.0238094069 -0.045851921 -0.0017668927 1.999484e-02
## MCI:[70,75)-CTL:[60,65) -0.0131825573 -0.026264501 -0.0001006135 4.598849e-02
## CTL:[70,75)-CTL:[60,65) -0.0211804136 -0.033383719 -0.0089771086 6.768297e-07
## AD:[75,80)-CTL:[60,65)  -0.0260776695 -0.044027882 -0.0081274567 9.597817e-05
## MCI:[75,80)-CTL:[60,65) -0.0213735546 -0.038145393 -0.0046017161 1.506187e-03
## CTL:[75,80)-CTL:[60,65) -0.0108047376 -0.025402749  0.0037932740 4.465391e-01
## AD:[80,85)-CTL:[60,65)  -0.0256751938 -0.047717708 -0.0036326796 6.871740e-03
## MCI:[80,85)-CTL:[60,65) -0.0323206152 -0.054363129 -0.0102781010 7.693327e-05
## CTL:[80,85)-CTL:[60,65) -0.0057293299 -0.041842816  0.0303841564 1.000000e+00
## AD:[85,90)-CTL:[60,65)  -0.0492991610 -0.085412647 -0.0131856747 3.865864e-04
## MCI:[85,90)-CTL:[60,65)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[60,65)            NA           NA            NA           NA
## MCI:[65,70)-AD:[65,70)             NA           NA            NA           NA
## CTL:[65,70)-AD:[65,70)             NA           NA            NA           NA
## AD:[70,75)-AD:[65,70)              NA           NA            NA           NA
## MCI:[70,75)-AD:[65,70)             NA           NA            NA           NA
## CTL:[70,75)-AD:[65,70)             NA           NA            NA           NA
## AD:[75,80)-AD:[65,70)              NA           NA            NA           NA
## MCI:[75,80)-AD:[65,70)             NA           NA            NA           NA
## CTL:[75,80)-AD:[65,70)             NA           NA            NA           NA
## AD:[80,85)-AD:[65,70)              NA           NA            NA           NA
## MCI:[80,85)-AD:[65,70)             NA           NA            NA           NA
## CTL:[80,85)-AD:[65,70)             NA           NA            NA           NA
## AD:[85,90)-AD:[65,70)              NA           NA            NA           NA
## MCI:[85,90)-AD:[65,70)             NA           NA            NA           NA
## CTL:[85,90)-AD:[65,70)             NA           NA            NA           NA
## CTL:[65,70)-MCI:[65,70)  0.0037636236 -0.010054457  0.0175817039 9.999605e-01
## AD:[70,75)-MCI:[65,70)  -0.0190298309 -0.042092841  0.0040331795 2.530061e-01
## MCI:[70,75)-MCI:[65,70) -0.0084029813 -0.023139578  0.0063336151 8.580698e-01
## CTL:[70,75)-MCI:[65,70) -0.0164008376 -0.030363311 -0.0024383639 6.032595e-03
## AD:[75,80)-MCI:[65,70)  -0.0212980935 -0.040487678 -0.0021085088 1.389610e-02
## MCI:[75,80)-MCI:[65,70) -0.0165939785 -0.034686092  0.0014981354 1.155479e-01
## CTL:[75,80)-MCI:[65,70) -0.0060251616 -0.022122738  0.0100724147 9.975039e-01
## AD:[80,85)-MCI:[65,70)  -0.0208956178 -0.043958628  0.0021673926 1.281810e-01
## MCI:[80,85)-MCI:[65,70) -0.0275410392 -0.050604050 -0.0044780287 4.640981e-03
## CTL:[80,85)-MCI:[65,70) -0.0009497539 -0.037695011  0.0357955031 1.000000e+00
## AD:[85,90)-MCI:[65,70)  -0.0445195850 -0.081264842 -0.0077743280 3.658894e-03
## MCI:[85,90)-MCI:[65,70)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[65,70)            NA           NA            NA           NA
## AD:[70,75)-CTL:[65,70)  -0.0227934545 -0.044641758 -0.0009451513 3.086157e-02
## MCI:[70,75)-CTL:[65,70) -0.0121666049 -0.024918592  0.0005853822 8.071838e-02
## CTL:[70,75)-CTL:[65,70) -0.0201644612 -0.032013367 -0.0083155552 1.265567e-06
## AD:[75,80)-CTL:[65,70)  -0.0250617171 -0.042772902 -0.0073505324 1.764978e-04
## MCI:[75,80)-CTL:[65,70) -0.0203576022 -0.036873367 -0.0038418373 2.731578e-03
## CTL:[75,80)-CTL:[65,70) -0.0097887852 -0.024091857  0.0045142867 5.940247e-01
## AD:[80,85)-CTL:[65,70)  -0.0246592414 -0.046507545 -0.0028109382 1.097228e-02
## MCI:[80,85)-CTL:[65,70) -0.0313046628 -0.053152966 -0.0094563596 1.326660e-04
## CTL:[80,85)-CTL:[65,70) -0.0047133775 -0.040708652  0.0312818974 1.000000e+00
## AD:[85,90)-CTL:[65,70)  -0.0482832086 -0.084278484 -0.0122879337 5.564458e-04
## MCI:[85,90)-CTL:[65,70)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[65,70)            NA           NA            NA           NA
## MCI:[70,75)-AD:[70,75)   0.0106268496 -0.011813656  0.0330673551 9.692717e-01
## CTL:[70,75)-AD:[70,75)   0.0026289933 -0.019310917  0.0245689041 1.000000e+00
## AD:[75,80)-AD:[70,75)   -0.0022682625 -0.027854375  0.0233178503 1.000000e+00
## MCI:[75,80)-AD:[70,75)   0.0024358524 -0.022337795  0.0272094996 1.000000e+00
## CTL:[75,80)-AD:[70,75)   0.0130046693 -0.010352149  0.0363614879 8.804043e-01
## AD:[80,85)-AD:[70,75)   -0.0018657869 -0.030471931  0.0267403569 1.000000e+00
## MCI:[80,85)-AD:[70,75)  -0.0085112083 -0.037117352  0.0200949355 9.998660e-01
## CTL:[80,85)-AD:[70,75)   0.0180800770 -0.022375120  0.0585352736 9.825094e-01
## AD:[85,90)-AD:[70,75)   -0.0254897541 -0.065944951  0.0149654424 7.328777e-01
## MCI:[85,90)-AD:[70,75)             NA           NA            NA           NA
## CTL:[85,90)-AD:[70,75)             NA           NA            NA           NA
## CTL:[70,75)-MCI:[70,75) -0.0079978563 -0.020906168  0.0049104552 7.574760e-01
## AD:[75,80)-MCI:[70,75)  -0.0128951121 -0.031331869  0.0055416444 5.538704e-01
## MCI:[75,80)-MCI:[70,75) -0.0081909972 -0.025482568  0.0091005735 9.691848e-01
## CTL:[75,80)-MCI:[70,75)  0.0023778197 -0.012814474  0.0175701137 1.000000e+00
## AD:[80,85)-MCI:[70,75)  -0.0124926365 -0.034933142  0.0099478690 8.805340e-01
## MCI:[80,85)-MCI:[70,75) -0.0191380579 -0.041578563  0.0033024476 2.033763e-01
## CTL:[80,85)-MCI:[70,75)  0.0074532274 -0.028904547  0.0438110017 9.999994e-01
## AD:[85,90)-MCI:[70,75)  -0.0361166037 -0.072474378  0.0002411705 5.369859e-02
## MCI:[85,90)-MCI:[70,75)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[70,75)            NA           NA            NA           NA
## AD:[75,80)-CTL:[70,75)  -0.0048972559 -0.022721324  0.0129268119 9.999553e-01
## MCI:[75,80)-CTL:[70,75) -0.0001931409 -0.016829902  0.0164436201 1.000000e+00
## CTL:[75,80)-CTL:[70,75)  0.0103756760 -0.004066941  0.0248182933 5.033192e-01
## AD:[80,85)-CTL:[70,75)  -0.0044947802 -0.026434691  0.0174451306 9.999994e-01
## MCI:[80,85)-CTL:[70,75) -0.0111402016 -0.033080112  0.0107997092 9.425345e-01
## CTL:[80,85)-CTL:[70,75)  0.0154510837 -0.020599868  0.0515020358 9.886314e-01
## AD:[85,90)-CTL:[70,75)  -0.0281187474 -0.064169700  0.0079322047 3.489203e-01
## MCI:[85,90)-CTL:[70,75)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[70,75)            NA           NA            NA           NA
## MCI:[75,80)-AD:[75,80)   0.0047041149 -0.016510769  0.0259189990 9.999980e-01
## CTL:[75,80)-AD:[75,80)   0.0152729318 -0.004268785  0.0348146483 3.452659e-01
## AD:[80,85)-AD:[75,80)    0.0004024756 -0.025183637  0.0259885885 1.000000e+00
## MCI:[80,85)-AD:[75,80)  -0.0062429457 -0.031829059  0.0193431671 9.999918e-01
## CTL:[80,85)-AD:[75,80)   0.0203483395 -0.018030830  0.0587275088 9.178325e-01
## AD:[85,90)-AD:[75,80)   -0.0232214916 -0.061600661  0.0151576777 7.898732e-01
## MCI:[85,90)-AD:[75,80)             NA           NA            NA           NA
## CTL:[85,90)-AD:[75,80)             NA           NA            NA           NA
## CTL:[75,80)-MCI:[75,80)  0.0105688169 -0.007896370  0.0290340033 8.542780e-01
## AD:[80,85)-MCI:[75,80)  -0.0043016393 -0.029075287  0.0204720080 1.000000e+00
## MCI:[80,85)-MCI:[75,80) -0.0109470607 -0.035720708  0.0138265866 9.844039e-01
## CTL:[80,85)-MCI:[75,80)  0.0156442246 -0.022198147  0.0534865959 9.922725e-01
## AD:[85,90)-MCI:[75,80)  -0.0279256065 -0.065767978  0.0099167648 4.522147e-01
## MCI:[85,90)-MCI:[75,80)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[75,80)            NA           NA            NA           NA
## AD:[80,85)-CTL:[75,80)  -0.0148704562 -0.038227275  0.0084863624 7.169454e-01
## MCI:[80,85)-CTL:[75,80) -0.0215158776 -0.044872696  0.0018409411 1.112848e-01
## CTL:[80,85)-CTL:[75,80)  0.0050754077 -0.031854965  0.0420057806 1.000000e+00
## AD:[85,90)-CTL:[75,80)  -0.0384944234 -0.075424796 -0.0015640505 3.118513e-02
## MCI:[85,90)-CTL:[75,80)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[75,80)            NA           NA            NA           NA
## MCI:[80,85)-AD:[80,85)  -0.0066454214 -0.035251565  0.0219607224 9.999960e-01
## CTL:[80,85)-AD:[80,85)   0.0199458639 -0.020509333  0.0604010605 9.555534e-01
## AD:[85,90)-AD:[80,85)   -0.0236239672 -0.064079164  0.0168312293 8.328823e-01
## MCI:[85,90)-AD:[80,85)             NA           NA            NA           NA
## CTL:[85,90)-AD:[80,85)             NA           NA            NA           NA
## CTL:[80,85)-MCI:[80,85)  0.0265912853 -0.013863911  0.0670464818 6.651102e-01
## AD:[85,90)-MCI:[80,85)  -0.0169785458 -0.057433742  0.0234766507 9.909039e-01
## MCI:[85,90)-MCI:[80,85)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[80,85)            NA           NA            NA           NA
## AD:[85,90)-CTL:[80,85)  -0.0435698311 -0.093117126  0.0059774634 1.626629e-01
## MCI:[85,90)-CTL:[80,85)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[80,85)            NA           NA            NA           NA
## MCI:[85,90)-AD:[85,90)             NA           NA            NA           NA
## CTL:[85,90)-AD:[85,90)             NA           NA            NA           NA
## CTL:[85,90)-MCI:[85,90)            NA           NA            NA           NA
ggplot(filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"), aes(x = Age_interval, y = K, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot()  + theme_pubr()

aov_diag2 <- aov(logAvgThickness ~ Diagnostic*Age_interval, data = filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"))
summary(aov_diag2)
##                          Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic                2 0.00951 0.004754  24.206 3.90e-10 ***
## Age_interval              5 0.01008 0.002016  10.266 9.08e-09 ***
## Diagnostic:Age_interval   7 0.00581 0.000830   4.224 0.000228 ***
## Residuals               199 0.03909 0.000196                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov_diag_2_diag_TK <- TukeyHSD(aov_diag2)
aov_diag_2_diag_TK$`Diagnostic:Age_interval`
##                                  diff          lwr           upr        p adj
## MCI:[60,65)-AD:[60,65)   0.0247295988 -0.024817696  0.0742768933 9.504734e-01
## CTL:[60,65)-AD:[60,65)   0.0440618281  0.007948342  0.0801753144 3.252613e-03
## AD:[65,70)-AD:[60,65)              NA           NA            NA           NA
## MCI:[65,70)-AD:[60,65)   0.0392822521  0.002536995  0.0760275091 2.283589e-02
## CTL:[65,70)-AD:[60,65)   0.0430458757  0.007050601  0.0790411506 4.534547e-03
## AD:[70,75)-AD:[60,65)    0.0202524211 -0.020202775  0.0607076177 9.491546e-01
## MCI:[70,75)-AD:[60,65)   0.0308792707 -0.005478504  0.0672370450 2.092742e-01
## CTL:[70,75)-AD:[60,65)   0.0228814145 -0.013169538  0.0589323666 7.217380e-01
## AD:[75,80)-AD:[60,65)    0.0179841586 -0.020395011  0.0563633279 9.721875e-01
## MCI:[75,80)-AD:[60,65)   0.0226882735 -0.015154098  0.0605306448 8.015808e-01
## CTL:[75,80)-AD:[60,65)   0.0332570904 -0.003673282  0.0701874633 1.347703e-01
## AD:[80,85)-AD:[60,65)    0.0183866342 -0.022068562  0.0588418308 9.793185e-01
## MCI:[80,85)-AD:[60,65)   0.0117412129 -0.028713984  0.0521964094 9.999045e-01
## CTL:[80,85)-AD:[60,65)   0.0383324981 -0.011214796  0.0878797926 3.636968e-01
## AD:[85,90)-AD:[60,65)   -0.0052373330 -0.054784627  0.0443099615 1.000000e+00
## MCI:[85,90)-AD:[60,65)             NA           NA            NA           NA
## CTL:[85,90)-AD:[60,65)             NA           NA            NA           NA
## CTL:[60,65)-MCI:[60,65)  0.0193322293 -0.016781257  0.0554457156 9.113170e-01
## AD:[65,70)-MCI:[60,65)             NA           NA            NA           NA
## MCI:[65,70)-MCI:[60,65)  0.0145526533 -0.022192604  0.0512979103 9.952006e-01
## CTL:[65,70)-MCI:[60,65)  0.0183162769 -0.017678998  0.0543115518 9.414707e-01
## AD:[70,75)-MCI:[60,65)  -0.0044771777 -0.044932374  0.0359780189 1.000000e+00
## MCI:[70,75)-MCI:[60,65)  0.0061496719 -0.030208102  0.0425074462 1.000000e+00
## CTL:[70,75)-MCI:[60,65) -0.0018481843 -0.037899136  0.0342027678 1.000000e+00
## AD:[75,80)-MCI:[60,65)  -0.0067454402 -0.045124609  0.0316337291 9.999999e-01
## MCI:[75,80)-MCI:[60,65) -0.0020413253 -0.039883697  0.0358010460 1.000000e+00
## CTL:[75,80)-MCI:[60,65)  0.0085274916 -0.028402881  0.0454578645 9.999964e-01
## AD:[80,85)-MCI:[60,65)  -0.0063429646 -0.046798161  0.0341122320 1.000000e+00
## MCI:[80,85)-MCI:[60,65) -0.0129883859 -0.053443582  0.0274668106 9.996311e-01
## CTL:[80,85)-MCI:[60,65)  0.0136028994 -0.035944395  0.0631501939 9.999558e-01
## AD:[85,90)-MCI:[60,65)  -0.0299669318 -0.079514226  0.0195803627 7.903899e-01
## MCI:[85,90)-MCI:[60,65)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[60,65)            NA           NA            NA           NA
## AD:[65,70)-CTL:[60,65)             NA           NA            NA           NA
## MCI:[65,70)-CTL:[60,65) -0.0047795760 -0.018902728  0.0093435759 9.992738e-01
## CTL:[65,70)-CTL:[60,65) -0.0010159524 -0.013053781  0.0110218764 1.000000e+00
## AD:[70,75)-CTL:[60,65)  -0.0238094069 -0.045851921 -0.0017668927 1.999484e-02
## MCI:[70,75)-CTL:[60,65) -0.0131825573 -0.026264501 -0.0001006135 4.598849e-02
## CTL:[70,75)-CTL:[60,65) -0.0211804136 -0.033383719 -0.0089771086 6.768297e-07
## AD:[75,80)-CTL:[60,65)  -0.0260776695 -0.044027882 -0.0081274567 9.597817e-05
## MCI:[75,80)-CTL:[60,65) -0.0213735546 -0.038145393 -0.0046017161 1.506187e-03
## CTL:[75,80)-CTL:[60,65) -0.0108047376 -0.025402749  0.0037932740 4.465391e-01
## AD:[80,85)-CTL:[60,65)  -0.0256751938 -0.047717708 -0.0036326796 6.871740e-03
## MCI:[80,85)-CTL:[60,65) -0.0323206152 -0.054363129 -0.0102781010 7.693327e-05
## CTL:[80,85)-CTL:[60,65) -0.0057293299 -0.041842816  0.0303841564 1.000000e+00
## AD:[85,90)-CTL:[60,65)  -0.0492991610 -0.085412647 -0.0131856747 3.865864e-04
## MCI:[85,90)-CTL:[60,65)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[60,65)            NA           NA            NA           NA
## MCI:[65,70)-AD:[65,70)             NA           NA            NA           NA
## CTL:[65,70)-AD:[65,70)             NA           NA            NA           NA
## AD:[70,75)-AD:[65,70)              NA           NA            NA           NA
## MCI:[70,75)-AD:[65,70)             NA           NA            NA           NA
## CTL:[70,75)-AD:[65,70)             NA           NA            NA           NA
## AD:[75,80)-AD:[65,70)              NA           NA            NA           NA
## MCI:[75,80)-AD:[65,70)             NA           NA            NA           NA
## CTL:[75,80)-AD:[65,70)             NA           NA            NA           NA
## AD:[80,85)-AD:[65,70)              NA           NA            NA           NA
## MCI:[80,85)-AD:[65,70)             NA           NA            NA           NA
## CTL:[80,85)-AD:[65,70)             NA           NA            NA           NA
## AD:[85,90)-AD:[65,70)              NA           NA            NA           NA
## MCI:[85,90)-AD:[65,70)             NA           NA            NA           NA
## CTL:[85,90)-AD:[65,70)             NA           NA            NA           NA
## CTL:[65,70)-MCI:[65,70)  0.0037636236 -0.010054457  0.0175817039 9.999605e-01
## AD:[70,75)-MCI:[65,70)  -0.0190298309 -0.042092841  0.0040331795 2.530061e-01
## MCI:[70,75)-MCI:[65,70) -0.0084029813 -0.023139578  0.0063336151 8.580698e-01
## CTL:[70,75)-MCI:[65,70) -0.0164008376 -0.030363311 -0.0024383639 6.032595e-03
## AD:[75,80)-MCI:[65,70)  -0.0212980935 -0.040487678 -0.0021085088 1.389610e-02
## MCI:[75,80)-MCI:[65,70) -0.0165939785 -0.034686092  0.0014981354 1.155479e-01
## CTL:[75,80)-MCI:[65,70) -0.0060251616 -0.022122738  0.0100724147 9.975039e-01
## AD:[80,85)-MCI:[65,70)  -0.0208956178 -0.043958628  0.0021673926 1.281810e-01
## MCI:[80,85)-MCI:[65,70) -0.0275410392 -0.050604050 -0.0044780287 4.640981e-03
## CTL:[80,85)-MCI:[65,70) -0.0009497539 -0.037695011  0.0357955031 1.000000e+00
## AD:[85,90)-MCI:[65,70)  -0.0445195850 -0.081264842 -0.0077743280 3.658894e-03
## MCI:[85,90)-MCI:[65,70)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[65,70)            NA           NA            NA           NA
## AD:[70,75)-CTL:[65,70)  -0.0227934545 -0.044641758 -0.0009451513 3.086157e-02
## MCI:[70,75)-CTL:[65,70) -0.0121666049 -0.024918592  0.0005853822 8.071838e-02
## CTL:[70,75)-CTL:[65,70) -0.0201644612 -0.032013367 -0.0083155552 1.265567e-06
## AD:[75,80)-CTL:[65,70)  -0.0250617171 -0.042772902 -0.0073505324 1.764978e-04
## MCI:[75,80)-CTL:[65,70) -0.0203576022 -0.036873367 -0.0038418373 2.731578e-03
## CTL:[75,80)-CTL:[65,70) -0.0097887852 -0.024091857  0.0045142867 5.940247e-01
## AD:[80,85)-CTL:[65,70)  -0.0246592414 -0.046507545 -0.0028109382 1.097228e-02
## MCI:[80,85)-CTL:[65,70) -0.0313046628 -0.053152966 -0.0094563596 1.326660e-04
## CTL:[80,85)-CTL:[65,70) -0.0047133775 -0.040708652  0.0312818974 1.000000e+00
## AD:[85,90)-CTL:[65,70)  -0.0482832086 -0.084278484 -0.0122879337 5.564458e-04
## MCI:[85,90)-CTL:[65,70)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[65,70)            NA           NA            NA           NA
## MCI:[70,75)-AD:[70,75)   0.0106268496 -0.011813656  0.0330673551 9.692717e-01
## CTL:[70,75)-AD:[70,75)   0.0026289933 -0.019310917  0.0245689041 1.000000e+00
## AD:[75,80)-AD:[70,75)   -0.0022682625 -0.027854375  0.0233178503 1.000000e+00
## MCI:[75,80)-AD:[70,75)   0.0024358524 -0.022337795  0.0272094996 1.000000e+00
## CTL:[75,80)-AD:[70,75)   0.0130046693 -0.010352149  0.0363614879 8.804043e-01
## AD:[80,85)-AD:[70,75)   -0.0018657869 -0.030471931  0.0267403569 1.000000e+00
## MCI:[80,85)-AD:[70,75)  -0.0085112083 -0.037117352  0.0200949355 9.998660e-01
## CTL:[80,85)-AD:[70,75)   0.0180800770 -0.022375120  0.0585352736 9.825094e-01
## AD:[85,90)-AD:[70,75)   -0.0254897541 -0.065944951  0.0149654424 7.328777e-01
## MCI:[85,90)-AD:[70,75)             NA           NA            NA           NA
## CTL:[85,90)-AD:[70,75)             NA           NA            NA           NA
## CTL:[70,75)-MCI:[70,75) -0.0079978563 -0.020906168  0.0049104552 7.574760e-01
## AD:[75,80)-MCI:[70,75)  -0.0128951121 -0.031331869  0.0055416444 5.538704e-01
## MCI:[75,80)-MCI:[70,75) -0.0081909972 -0.025482568  0.0091005735 9.691848e-01
## CTL:[75,80)-MCI:[70,75)  0.0023778197 -0.012814474  0.0175701137 1.000000e+00
## AD:[80,85)-MCI:[70,75)  -0.0124926365 -0.034933142  0.0099478690 8.805340e-01
## MCI:[80,85)-MCI:[70,75) -0.0191380579 -0.041578563  0.0033024476 2.033763e-01
## CTL:[80,85)-MCI:[70,75)  0.0074532274 -0.028904547  0.0438110017 9.999994e-01
## AD:[85,90)-MCI:[70,75)  -0.0361166037 -0.072474378  0.0002411705 5.369859e-02
## MCI:[85,90)-MCI:[70,75)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[70,75)            NA           NA            NA           NA
## AD:[75,80)-CTL:[70,75)  -0.0048972559 -0.022721324  0.0129268119 9.999553e-01
## MCI:[75,80)-CTL:[70,75) -0.0001931409 -0.016829902  0.0164436201 1.000000e+00
## CTL:[75,80)-CTL:[70,75)  0.0103756760 -0.004066941  0.0248182933 5.033192e-01
## AD:[80,85)-CTL:[70,75)  -0.0044947802 -0.026434691  0.0174451306 9.999994e-01
## MCI:[80,85)-CTL:[70,75) -0.0111402016 -0.033080112  0.0107997092 9.425345e-01
## CTL:[80,85)-CTL:[70,75)  0.0154510837 -0.020599868  0.0515020358 9.886314e-01
## AD:[85,90)-CTL:[70,75)  -0.0281187474 -0.064169700  0.0079322047 3.489203e-01
## MCI:[85,90)-CTL:[70,75)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[70,75)            NA           NA            NA           NA
## MCI:[75,80)-AD:[75,80)   0.0047041149 -0.016510769  0.0259189990 9.999980e-01
## CTL:[75,80)-AD:[75,80)   0.0152729318 -0.004268785  0.0348146483 3.452659e-01
## AD:[80,85)-AD:[75,80)    0.0004024756 -0.025183637  0.0259885885 1.000000e+00
## MCI:[80,85)-AD:[75,80)  -0.0062429457 -0.031829059  0.0193431671 9.999918e-01
## CTL:[80,85)-AD:[75,80)   0.0203483395 -0.018030830  0.0587275088 9.178325e-01
## AD:[85,90)-AD:[75,80)   -0.0232214916 -0.061600661  0.0151576777 7.898732e-01
## MCI:[85,90)-AD:[75,80)             NA           NA            NA           NA
## CTL:[85,90)-AD:[75,80)             NA           NA            NA           NA
## CTL:[75,80)-MCI:[75,80)  0.0105688169 -0.007896370  0.0290340033 8.542780e-01
## AD:[80,85)-MCI:[75,80)  -0.0043016393 -0.029075287  0.0204720080 1.000000e+00
## MCI:[80,85)-MCI:[75,80) -0.0109470607 -0.035720708  0.0138265866 9.844039e-01
## CTL:[80,85)-MCI:[75,80)  0.0156442246 -0.022198147  0.0534865959 9.922725e-01
## AD:[85,90)-MCI:[75,80)  -0.0279256065 -0.065767978  0.0099167648 4.522147e-01
## MCI:[85,90)-MCI:[75,80)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[75,80)            NA           NA            NA           NA
## AD:[80,85)-CTL:[75,80)  -0.0148704562 -0.038227275  0.0084863624 7.169454e-01
## MCI:[80,85)-CTL:[75,80) -0.0215158776 -0.044872696  0.0018409411 1.112848e-01
## CTL:[80,85)-CTL:[75,80)  0.0050754077 -0.031854965  0.0420057806 1.000000e+00
## AD:[85,90)-CTL:[75,80)  -0.0384944234 -0.075424796 -0.0015640505 3.118513e-02
## MCI:[85,90)-CTL:[75,80)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[75,80)            NA           NA            NA           NA
## MCI:[80,85)-AD:[80,85)  -0.0066454214 -0.035251565  0.0219607224 9.999960e-01
## CTL:[80,85)-AD:[80,85)   0.0199458639 -0.020509333  0.0604010605 9.555534e-01
## AD:[85,90)-AD:[80,85)   -0.0236239672 -0.064079164  0.0168312293 8.328823e-01
## MCI:[85,90)-AD:[80,85)             NA           NA            NA           NA
## CTL:[85,90)-AD:[80,85)             NA           NA            NA           NA
## CTL:[80,85)-MCI:[80,85)  0.0265912853 -0.013863911  0.0670464818 6.651102e-01
## AD:[85,90)-MCI:[80,85)  -0.0169785458 -0.057433742  0.0234766507 9.909039e-01
## MCI:[85,90)-MCI:[80,85)            NA           NA            NA           NA
## CTL:[85,90)-MCI:[80,85)            NA           NA            NA           NA
## AD:[85,90)-CTL:[80,85)  -0.0435698311 -0.093117126  0.0059774634 1.626629e-01
## MCI:[85,90)-CTL:[80,85)            NA           NA            NA           NA
## CTL:[85,90)-CTL:[80,85)            NA           NA            NA           NA
## MCI:[85,90)-AD:[85,90)             NA           NA            NA           NA
## CTL:[85,90)-AD:[85,90)             NA           NA            NA           NA
## CTL:[85,90)-MCI:[85,90)            NA           NA            NA           NA
ggplot(dados_hemi_v1, aes(x = Diagnostic, y = K_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot() +
 stat_compare_means(method = "anova") + theme_pubr()

ggplot(dados_hemi_v1, aes(x = Diagnostic, y = S_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot()  + theme_pubr()

ggplot(dados_hemi_v1, aes(x= S_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_density() + theme_pubr()

Is it easier to diff diag when younger?

ggplot(filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"), aes(x = Age_interval, y = logAvgThickness_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot()  + theme_pubr()

11.0.3 Podemos localizar partes com maior detrimento por idade e doenca?

11.0.3.1 Corrigindo pela curvatura

Diagnostic term estimate std.error statistic p.value conf.low conf.high
AD (Intercept) -0.038 0.069 -0.546 0.586 -0.175 0.100
AD logExposedArea_corrected 1.139 0.016 72.633 0.000 1.108 1.170
MCI (Intercept) -0.092 0.043 -2.157 0.032 -0.176 -0.008
MCI logExposedArea_corrected 1.155 0.010 120.088 0.000 1.136 1.174
CTL (Intercept) -0.108 0.030 -3.658 0.000 -0.166 -0.050
CTL logExposedArea_corrected 1.160 0.007 173.625 0.000 1.147 1.173
ROI Diagnostic term estimate std.error statistic p.value conf.low conf.high
F AD (Intercept) 0.09 0.51 0.17 0.87 -0.96 1.13
F AD logExposedArea_corrected 1.11 0.11 9.74 0.00 0.87 1.34
F MCI (Intercept) 0.41 0.22 1.82 0.07 -0.04 0.85
F MCI logExposedArea_corrected 1.04 0.05 20.81 0.00 0.94 1.14
F CTL (Intercept) -0.07 0.19 -0.36 0.72 -0.45 0.31
F CTL logExposedArea_corrected 1.14 0.04 26.40 0.00 1.06 1.23
O AD (Intercept) 0.39 0.41 0.95 0.35 -0.46 1.23
O AD logExposedArea_corrected 1.04 0.10 10.60 0.00 0.84 1.24
O MCI (Intercept) 0.39 0.18 2.19 0.03 0.03 0.75
O MCI logExposedArea_corrected 1.04 0.04 24.18 0.00 0.95 1.13
O CTL (Intercept) -0.03 0.15 -0.17 0.87 -0.32 0.27
O CTL logExposedArea_corrected 1.14 0.04 31.72 0.00 1.07 1.21
P AD (Intercept) 0.51 0.28 1.85 0.08 -0.06 1.08
P AD logExposedArea_corrected 1.02 0.06 17.12 0.00 0.90 1.15
P MCI (Intercept) 0.37 0.20 1.83 0.07 -0.03 0.78
P MCI logExposedArea_corrected 1.06 0.04 23.83 0.00 0.97 1.15
P CTL (Intercept) 0.28 0.14 1.99 0.05 0.00 0.56
P CTL logExposedArea_corrected 1.08 0.03 35.14 0.00 1.02 1.14
T AD (Intercept) 0.63 0.39 1.65 0.11 -0.16 1.43
T AD logExposedArea_corrected 0.99 0.09 11.41 0.00 0.81 1.17
T MCI (Intercept) 0.24 0.19 1.23 0.22 -0.15 0.63
T MCI logExposedArea_corrected 1.08 0.04 24.85 0.00 0.99 1.17
T CTL (Intercept) 0.12 0.16 0.76 0.45 -0.20 0.45
T CTL logExposedArea_corrected 1.11 0.04 30.31 0.00 1.04 1.18

11.0.3.2 Corrigindo pela idade

##   diag        x
## 1   AD 1.042579
## 2  MCI 1.043525
## 3  CTL 1.038388
ROI Diagnostic term estimate std.error statistic p.value conf.low conf.high
F AD (Intercept) 0.52 0.40 1.31 0.20 -0.30 1.35
F AD logExposedArea_age_decay 1.02 0.09 10.75 0.00 0.82 1.21
F MCI (Intercept) 0.70 0.22 3.25 0.00 0.27 1.14
F MCI logExposedArea_age_decay 0.98 0.05 19.06 0.00 0.87 1.08
F CTL (Intercept) 0.42 0.17 2.55 0.01 0.10 0.75
F CTL logExposedArea_age_decay 1.04 0.04 26.59 0.00 0.97 1.12
O AD (Intercept) 0.72 0.39 1.85 0.08 -0.09 1.53
O AD logExposedArea_age_decay 0.96 0.10 9.16 0.00 0.74 1.18
O MCI (Intercept) 0.47 0.17 2.70 0.01 0.12 0.81
O MCI logExposedArea_age_decay 1.03 0.05 22.12 0.00 0.94 1.13
O CTL (Intercept) 0.81 0.13 6.17 0.00 0.55 1.07
O CTL logExposedArea_age_decay 0.94 0.04 26.69 0.00 0.87 1.01
P AD (Intercept) 0.20 0.32 0.62 0.54 -0.47 0.87
P AD logExposedArea_age_decay 1.11 0.08 13.77 0.00 0.95 1.28
P MCI (Intercept) 0.87 0.23 3.73 0.00 0.40 1.33
P MCI logExposedArea_age_decay 0.95 0.06 16.41 0.00 0.84 1.07
P CTL (Intercept) 0.45 0.17 2.64 0.01 0.11 0.78
P CTL logExposedArea_age_decay 1.06 0.04 25.02 0.00 0.97 1.14
T AD (Intercept) 0.49 0.40 1.22 0.23 -0.34 1.33
T AD logExposedArea_age_decay 1.04 0.10 10.14 0.00 0.83 1.25
T MCI (Intercept) 0.80 0.19 4.28 0.00 0.43 1.18
T MCI logExposedArea_age_decay 0.96 0.05 20.28 0.00 0.87 1.06
T CTL (Intercept) 0.29 0.18 1.61 0.11 -0.07 0.64
T CTL logExposedArea_age_decay 1.09 0.05 23.96 0.00 1.00 1.18 

##    diag ROI         x
## 1    AD   F 1.0171086
## 2   MCI   F 0.9764493
## 3   CTL   F 1.0434639
## 4    AD   O 0.9610541
## 5   MCI   O 1.0319401
## 6   CTL   O 0.9410686
## 7    AD   P 1.1135527
## 8   MCI   P 0.9516712
## 9   CTL   P 1.0561775
## 10   AD   T 1.0359612
## 11  MCI   T 0.9606870
## 12  CTL   T 1.0920563
ROI Diagnostic term estimate std.error statistic p.value conf.low conf.high
F AD (Intercept) 0.52 0.40 1.31 0.20 -0.30 1.35
F AD logExposedArea_age_decay 1.02 0.09 10.75 0.00 0.82 1.21
F MCI (Intercept) 0.70 0.22 3.25 0.00 0.27 1.14
F MCI logExposedArea_age_decay 0.98 0.05 19.06 0.00 0.87 1.08
F CTL (Intercept) 0.42 0.17 2.55 0.01 0.10 0.75
F CTL logExposedArea_age_decay 1.04 0.04 26.59 0.00 0.97 1.12
O AD (Intercept) 0.72 0.39 1.85 0.08 -0.09 1.53
O AD logExposedArea_age_decay 0.96 0.10 9.16 0.00 0.74 1.18
O MCI (Intercept) 0.47 0.17 2.70 0.01 0.12 0.81
O MCI logExposedArea_age_decay 1.03 0.05 22.12 0.00 0.94 1.13
O CTL (Intercept) 0.81 0.13 6.17 0.00 0.55 1.07
O CTL logExposedArea_age_decay 0.94 0.04 26.69 0.00 0.87 1.01
P AD (Intercept) 0.20 0.32 0.62 0.54 -0.47 0.87
P AD logExposedArea_age_decay 1.11 0.08 13.77 0.00 0.95 1.28
P MCI (Intercept) 0.87 0.23 3.73 0.00 0.40 1.33
P MCI logExposedArea_age_decay 0.95 0.06 16.41 0.00 0.84 1.07
P CTL (Intercept) 0.45 0.17 2.64 0.01 0.11 0.78
P CTL logExposedArea_age_decay 1.06 0.04 25.02 0.00 0.97 1.14
T AD (Intercept) 0.49 0.40 1.22 0.23 -0.34 1.33
T AD logExposedArea_age_decay 1.04 0.10 10.14 0.00 0.83 1.25
T MCI (Intercept) 0.80 0.19 4.28 0.00 0.43 1.18
T MCI logExposedArea_age_decay 0.96 0.05 20.28 0.00 0.87 1.06
T CTL (Intercept) 0.29 0.18 1.61 0.11 -0.07 0.64
T CTL logExposedArea_age_decay 1.09 0.05 23.96 0.00 1.00 1.18

11.0.4 Comparing Anovas - diff and p-values

11.0.4.1 Comparing DA and CTL through age

aov_diag_age <- aov(K ~ Diagnostic*Age_interval10, data = dados_hemi_v1)
summary(aov_diag_age)
##                            Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic                  2 0.01121 0.005607  32.072 4.84e-13 ***
## Age_interval10              4 0.00595 0.001488   8.508 1.99e-06 ***
## Diagnostic:Age_interval10   4 0.00116 0.000289   1.652    0.162    
## Residuals                 235 0.04109 0.000175                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a <- TukeyHSD(aov_diag_age) 
a <- as.data.frame(a$`Diagnostic:Age_interval10`)
a %>% filter(`p adj` < 0.05 |`p adj` == 0.05 )
##                                diff         lwr           upr        p adj
## AD:[60,70)-CTL:[40,50)  -0.04120537 -0.07703904 -0.0053717114 8.972574e-03
## AD:[70,80)-CTL:[40,50)  -0.02683462 -0.04646153 -0.0072077197 4.533307e-04
## MCI:[70,80)-CTL:[40,50) -0.01807119 -0.03570285 -0.0004395211 3.841218e-02
## AD:[80,90)-CTL:[40,50)  -0.04980700 -0.07247020 -0.0271438040 1.112003e-10
## MCI:[80,90)-CTL:[40,50) -0.03425295 -0.05873200 -0.0097738963 2.796721e-04
## AD:[70,80)-CTL:[50,60)  -0.01813722 -0.03276625 -0.0035081902 2.784514e-03
## AD:[80,90)-CTL:[50,60)  -0.04110960 -0.05961402 -0.0226051750 6.702661e-11
## MCI:[80,90)-CTL:[50,60) -0.02555554 -0.04624412 -0.0048669700 2.961629e-03
## AD:[80,90)-MCI:[60,70)  -0.03228902 -0.05100254 -0.0135754971 1.205537e-06
## AD:[70,80)-CTL:[60,70)  -0.01400774 -0.02660211 -0.0014133700 1.422265e-02
## AD:[80,90)-CTL:[60,70)  -0.03698012 -0.05392188 -0.0200383548 1.523291e-10
## MCI:[80,90)-CTL:[60,70) -0.02142606 -0.04072960 -0.0021225208 1.461375e-02
## AD:[80,90)-AD:[70,80)   -0.02297238 -0.04259928 -0.0033454720 6.926793e-03
## AD:[80,90)-MCI:[70,80)  -0.03173582 -0.04936748 -0.0141041520 3.077961e-07
## AD:[80,90)-CTL:[70,80)  -0.03269266 -0.04990660 -0.0154787280 4.689844e-08
aov_diag_age <- aov(K_age_decay ~ Diagnostic*Age_interval10, data = dados_hemi_v1)
summary(aov_diag_age)
##                            Df  Sum Sq   Mean Sq F value   Pr(>F)    
## Diagnostic                  2 0.00478 0.0023923  16.351 2.25e-07 ***
## Age_interval10              4 0.00168 0.0004212   2.878   0.0235 *  
## Diagnostic:Age_interval10   4 0.00099 0.0002468   1.687   0.1537    
## Residuals                 235 0.03438 0.0001463                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
a <- TukeyHSD(aov_diag_age) 
a <- as.data.frame(a$`Diagnostic:Age_interval10`)
a %>% filter(`p adj` < 0.05 |`p adj` == 0.05 )
##                               diff         lwr          upr        p adj
## AD:[80,90)-CTL:[40,50) -0.02876988 -0.04950203 -0.008037726 3.334192e-04
## AD:[80,90)-CTL:[50,60) -0.02416190 -0.04108963 -0.007234172 1.816362e-04
## AD:[80,90)-MCI:[60,70) -0.02216979 -0.03928880 -0.005050780 1.295358e-03
## AD:[80,90)-CTL:[60,70) -0.02530581 -0.04080402 -0.009807590 6.202620e-06
## AD:[80,90)-MCI:[70,80) -0.02369527 -0.03982461 -0.007565935 9.433146e-05
## AD:[80,90)-CTL:[70,80) -0.02504436 -0.04079156 -0.009297159 1.283490e-05

11.0.5 S and I - AD and CTL

mean_K_I_S <-
  dados_hemi_v1 %>% group_by(Diagnostic) %>% summarise(
    mean.K = mean(Knorm, na.rm = TRUE),
    SD_K = sd(Knorm, na.rm = TRUE),
    mean.I = mean(Inorm, na.rm = TRUE),
    SD_I = sd(Inorm, na.rm = TRUE),
    mean.S = mean(Snorm, na.rm = TRUE),
    SD_S = sd(Snorm, na.rm = TRUE),
    # mean.K_age_decay = mean(Knorm_age_decay, na.rm = TRUE),
    # SD_K_age_decay = sd(Knorm_age_decay, na.rm = TRUE),
    # mean.I_age_decay = mean(Inorm_age_decay, na.rm = TRUE),
    # SD_I_age_decay = sd(Inorm_age_decay, na.rm = TRUE),
    # mean.S_age_decay = mean(Snorm_age_decay, na.rm = TRUE),
    # SD_S_age_decay = sd(Snorm_age_decay, na.rm = TRUE),
    N_SUBJ = n_distinct(SUBJ)
    )

mean_K_I_S_lobes <-
  dados_lobos_v1 %>% group_by(ROI, Diagnostic) %>% summarise(
    mean.K = mean(Knorm, na.rm = TRUE),
    SD_K = sd(Knorm, na.rm = TRUE),
    mean.I = mean(Inorm, na.rm = TRUE),
    SD_I = sd(Inorm, na.rm = TRUE),
    mean.S = mean(Snorm, na.rm = TRUE),
    SD_S = sd(Snorm, na.rm = TRUE),
    # mean.K_age_decay = mean(Knorm_age_decay, na.rm = TRUE),
    # SD_K_age_decay = sd(Knorm_age_decay, na.rm = TRUE),
    # mean.I_age_decay = mean(Inorm_age_decay, na.rm = TRUE),
    # SD_I_age_decay = sd(Inorm_age_decay, na.rm = TRUE),
    # mean.S_age_decay = mean(Snorm_age_decay, na.rm = TRUE),
    # SD_S_age_decay = sd(Snorm_age_decay, na.rm = TRUE),
    N_SUBJ = n_distinct(SUBJ)
    )

fig3a <- ggplot(mean_K_I_S, aes(x = mean.K, y = mean.S, color = Diagnostic)) +
  geom_point() +
  geom_line(group =1, color = "gray") +
  theme_pubr() +
  labs(x = "Mean K (norm)", y = "Mean S (norm)") + 
  theme(axis.title = element_text(size = 11),
    axis.text = element_text(size = 10), text = element_text(size = 10))

fig3c <- ggplot(mean_K_I_S, aes(x = mean.K, y = mean.I, color = Diagnostic)) +
  geom_point() +
  geom_line(group =1, color = "gray") +
  theme_pubr() +
  labs(x = "Mean K (norm)", y = "Mean I (norm)") +
  theme(axis.title = element_text(size = 11),
    axis.text = element_text(size = 10), text = element_text(size = 10))

lobes <- c(
    F = "Frontal Lobe",
    O = "Occipital Lobe",
    P = "Parietal Lobe",
    T = "Temporal Lobe"
)

dados_hemi_v1_DACTL <- dados_hemi_v1 %>%
  mutate(Age.group = ifelse(
    Age > 75,
    "76-86",
    ifelse((Age < 75 | Age == 75 & Age > 65 | Age == 65),
           "66-75",
           "")))

mean_K_I_S_ADCTL <-
  filter(dados_hemi_v1_DACTL) %>% group_by(Diagnostic, Age.group) %>% summarise(
    mean.T = mean(logAvgThickness, na.rm = TRUE),
    SD_T = sd(logAvgThickness, na.rm = TRUE),
    mean.K = mean(Knorm, na.rm = TRUE),
    SD_K = sd(Knorm, na.rm = TRUE),
    mean.I = mean(Inorm, na.rm = TRUE),
    SD_I = sd(Inorm, na.rm = TRUE),
    mean.S = mean(Snorm, na.rm = TRUE),
    SD_S = sd(Snorm, na.rm = TRUE),
    # mean.T_age_decay = mean(logAvgThickness_age_decay, na.rm = TRUE),
    # SD_T_age_decay = sd(logAvgThickness_age_decay, na.rm = TRUE),
    # mean.K_age_decay = mean(Knorm_age_decay, na.rm = TRUE),
    # SD_K_age_decay = sd(Knorm_age_decay, na.rm = TRUE),
    # mean.I_age_decay = mean(Inorm_age_decay, na.rm = TRUE),
    # SD_I_age_decay = sd(Inorm_age_decay, na.rm = TRUE),
    # mean.S_age_decay = mean(Snorm_age_decay, na.rm = TRUE),
    # SD_S_age_decay = sd(Snorm_age_decay, na.rm = TRUE),
    N_SUBJ = n_distinct(SUBJ)
  )

fig3b <- ggplot(mean_K_I_S_ADCTL, aes(x = mean.K, y = mean.S, color = Diagnostic, shape = Age.group ))+
    geom_point() +
    geom_line(aes(group = Diagnostic)) +
    #geom_text(aes(label=Age.group), nudge_y = 0.1, size =3)+
    theme_pubr() + 
  guides(color = FALSE) + 
    labs(x = "Mean K (norm)", y = "Mean S (norm)", shape = "Age") +
    theme(axis.title = element_text(size = 11),
          axis.text = element_text(size = 10), text = element_text(size = 8))

fig3d <- ggplot(mean_K_I_S_ADCTL, aes(x = mean.K, y = mean.I, color = Diagnostic, shape = Age.group ))+
    geom_point() +
    geom_line(aes(group = Diagnostic)) +
    #geom_text(aes(label=Age.group), nudge_y = 0.1, size =3)+
    theme_pubr() +
  guides(color = FALSE) + 
    labs(x = "Mean K (norm)", y = "Mean I (norm)", shape = "Age")  +
    theme(axis.title = element_text(size = 11),
          axis.text = element_text(size = 10), text = element_text(size = 8))

fig3 <- ggarrange(fig3a, fig3b, labels = c("A", "B"), nrow=2, ncol=1, font.label = list(size = 11))
fig3s <- ggarrange(fig3a, fig3b,fig3c, fig3d, labels = c("A", "B","C","D"), nrow=2, ncol=2, font.label = list(size = 11))

aK <- aov(K ~ Diagnostic*Age.group, data = dados_hemi_v1_DACTL)
summary(aK)
##                       Df  Sum Sq  Mean Sq F value   Pr(>F)    
## Diagnostic             2 0.01121 0.005607  28.933 5.53e-12 ***
## Age.group              1 0.00147 0.001472   7.595   0.0063 ** 
## Diagnostic:Age.group   2 0.00021 0.000103   0.532   0.5883    
## Residuals            240 0.04651 0.000194                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aK)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K ~ Diagnostic * Age.group, data = dados_hemi_v1_DACTL)
## 
## $Diagnostic
##                diff         lwr        upr     p adj
## MCI-AD  0.015650636 0.008048585 0.02325269 0.0000065
## CTL-AD  0.021969964 0.015008754 0.02893117 0.0000000
## CTL-MCI 0.006319328 0.001489023 0.01114963 0.0064052
## 
## $Age.group
##                    diff          lwr          upr     p adj
## 76-86-66-75 -0.00532469 -0.009494614 -0.001154767 0.0125435
## 
## $`Diagnostic:Age.group`
##                              diff          lwr          upr     p adj
## MCI:66-75-AD:66-75   0.0132027422 -0.002070637  0.028476122 0.1331506
## CTL:66-75-AD:66-75   0.0175295279  0.002973143  0.032085912 0.0083090
## AD:76-86-AD:66-75   -0.0071676449 -0.024162293  0.009827003 0.8308950
## MCI:76-86-AD:66-75   0.0039835621 -0.013011086  0.020978210 0.9847044
## CTL:76-86-AD:66-75   0.0135118232 -0.003219326  0.030242972 0.1900489
## CTL:66-75-MCI:66-75  0.0043267857 -0.002400958  0.011054529 0.4372864
## AD:76-86-MCI:66-75  -0.0203703871 -0.031424448 -0.009316327 0.0000040
## MCI:76-86-MCI:66-75 -0.0092191801 -0.020273241  0.001834880 0.1617523
## CTL:76-86-MCI:66-75  0.0003090811 -0.010335427  0.010953589 0.9999994
## AD:76-86-CTL:66-75  -0.0246971728 -0.034737315 -0.014657030 0.0000000
## MCI:76-86-CTL:66-75 -0.0135459658 -0.023586108 -0.003505823 0.0018865
## CTL:76-86-CTL:66-75 -0.0040177047 -0.013605079  0.005569670 0.8346759
## MCI:76-86-AD:76-86   0.0111512070 -0.002180492  0.024482906 0.1593089
## CTL:76-86-AD:76-86   0.0206794682  0.007685336  0.033673601 0.0001122
## CTL:76-86-MCI:76-86  0.0095282612 -0.003465871  0.022522394 0.2873585
aS <- aov(S ~ Diagnostic*Age.group, data = dados_hemi_v1_DACTL)
summary(aS)
##                       Df Sum Sq Mean Sq F value  Pr(>F)   
## Diagnostic             2 0.1539 0.07697   6.470 0.00183 **
## Age.group              1 0.0226 0.02262   1.901 0.16925   
## Diagnostic:Age.group   2 0.1029 0.05145   4.324 0.01429 * 
## Residuals            240 2.8555 0.01190                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aS)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = S ~ Diagnostic * Age.group, data = dados_hemi_v1_DACTL)
## 
## $Diagnostic
##                diff        lwr          upr     p adj
## MCI-AD  -0.04832085 -0.1078839  0.011242168 0.1371341
## CTL-AD  -0.07844671 -0.1329887 -0.023904762 0.0023323
## CTL-MCI -0.03012586 -0.0679719  0.007720179 0.1475039
## 
## $Age.group
##                   diff         lwr        upr     p adj
## 76-86-66-75 0.02087186 -0.01180001 0.05354373 0.2094573
## 
## $`Diagnostic:Age.group`
##                              diff          lwr          upr     p adj
## MCI:66-75-AD:66-75  -0.0953105293 -0.214979358  0.024358299 0.2029580
## CTL:66-75-AD:66-75  -0.0981648521 -0.212215936  0.015886232 0.1364256
## AD:76-86-AD:66-75   -0.0303712353 -0.163526415  0.102783945 0.9864784
## MCI:76-86-AD:66-75  -0.0001112472 -0.133266427  0.133043933 1.0000000
## CTL:76-86-AD:66-75  -0.1082371945 -0.239327828  0.022853439 0.1703321
## CTL:66-75-MCI:66-75 -0.0028543228 -0.055567029  0.049858384 0.9999872
## AD:76-86-MCI:66-75   0.0649392940 -0.021670645  0.151549233 0.2634887
## MCI:76-86-MCI:66-75  0.0951992821  0.008589343  0.181809221 0.0218904
## CTL:76-86-MCI:66-75 -0.0129266652 -0.096327707  0.070474377 0.9977733
## AD:76-86-CTL:66-75   0.0677936168 -0.010872150  0.146459384 0.1354651
## MCI:76-86-CTL:66-75  0.0980536048  0.019387838  0.176719372 0.0054879
## CTL:76-86-CTL:66-75 -0.0100723424 -0.085190617  0.065045932 0.9988905
## MCI:76-86-AD:76-86   0.0302599881 -0.074195529  0.134715505 0.9612657
## CTL:76-86-AD:76-86  -0.0778659592 -0.179676603  0.023944685 0.2428766
## CTL:76-86-MCI:76-86 -0.1081259473 -0.209936592 -0.006315303 0.0301371
11.0.5.0.1 Lobos
ggplot(dados_lobos_v1, aes(x = Diagnostic, y = S_corrected, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot() +
 stat_compare_means(method = "anova") + theme_pubr() + facet_wrap(ROI ~ . )

ggplot(dados_lobos_v1, aes(x = Diagnostic, y = I_corrected, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
  geom_boxplot() +
 stat_compare_means(method = "anova") + theme_pubr() + facet_wrap(ROI ~ . )

11.0.6 ANOVA

11.0.7 Sem MCI

11.1 Clinical data

#hemisphere
dados_hemi_v1_CD <- dados_hemi_v1 %>% 
  pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")

dados_hemi_v1_CD <- dados_hemi_v1_CD %>% 
  pivot_longer(c(K, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")

dados_hemi_v1_CD_behaviour <- unique(filter(dados_hemi_v1_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "DIGIT SPAN BACK"))

dados_hemi_v1_CD_biochq <- unique(filter(dados_hemi_v1_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina"))

#frontal lobe
dados_lobos_v1_F_CD <- dados_lobos_v1_F %>% 
  pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")

dados_lobos_v1_F_CD <- dados_lobos_v1_F_CD %>% 
  pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")

dados_lobos_v1_F_CD_behaviour <- filter(dados_lobos_v1_F_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK") 

dados_lobos_v1_F_CD_biochq <- filter(dados_lobos_v1_F_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")

#parietal lobe
dados_lobos_v1_P_CD <- dados_lobos_v1_P %>% 
  pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")

dados_lobos_v1_P_CD <- dados_lobos_v1_P_CD %>% 
  pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")

dados_lobos_v1_P_CD_behaviour <- filter(dados_lobos_v1_P_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK") 

dados_lobos_v1_P_CD_biochq <- filter(dados_lobos_v1_P_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")

#occipital lobe
dados_lobos_v1_O_CD <- dados_lobos_v1_O %>% 
  pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")

dados_lobos_v1_O_CD <- dados_lobos_v1_O_CD %>% 
  pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")

dados_lobos_v1_O_CD_behaviour <- filter(dados_lobos_v1_O_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK") 

dados_lobos_v1_O_CD_biochq <- filter(dados_lobos_v1_O_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")

#temporal lobe
dados_lobos_v1_T_CD <- dados_lobos_v1_T %>% 
  pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")

dados_lobos_v1_T_CD <- dados_lobos_v1_T_CD %>% 
  pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")

dados_lobos_v1_T_CD_behaviour <- filter(dados_lobos_v1_T_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK") 

dados_lobos_v1_T_CD_biochq <- filter(dados_lobos_v1_T_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")


sumario_dados_hemi_v1_diag_CD <-
dados_hemi_v1 %>%
group_by(Diagnostic) %>%
summarise(
N = n_distinct(SUBJ),
Mean_COGNITIVE_INDEX = mean(COGNITIVE_INDEX,  na.rm = TRUE),
STD_COGNITIVE_INDEX = sd(COGNITIVE_INDEX,  na.rm = TRUE),
Mean_A7_A5 = mean(`A7/A5`,  na.rm = TRUE),
STD_A7_A5 = sd(`A7/A5`,  na.rm = TRUE),
Mean_TMT_B_A = mean(`TMT B-A`,  na.rm = TRUE),
STD_TMT_B_A = sd(`TMT B-A`,  na.rm = TRUE),
Mean_relogio = mean(relogio,  na.rm = TRUE),
STD_relogio = sd(relogio,  na.rm = TRUE),
Mean_DIGIT_SPAN_BACK = mean(`DIGIT SPAN BACK`,  na.rm = TRUE),
STD_DIGIT_SPAN_BACK = sd(`DIGIT SPAN BACK`,  na.rm = TRUE),
Mean_Lipoxina = mean(Lipoxina ,  na.rm = TRUE),
STD_Lipoxina = sd(Lipoxina ,  na.rm = TRUE),
Mean_AB1_40 = mean(`AB1-40`,  na.rm = TRUE),
STD_AB1_40 = sd(`AB1-40`,  na.rm = TRUE),
Mean_AB1_42 = mean(`AB1-42`,  na.rm = TRUE),
STD_AB1_42 = sd(`AB1-42`,  na.rm = TRUE),
Mean_TAU = mean(TAU,  na.rm = TRUE),
STD_TAU = sd(TAU,  na.rm = TRUE))

sumario_dados_hemi_v1_diag_CD %>% kable(digits = 2) %>% kable_styling() %>%
  column_spec(1, width = "10cm")
Diagnostic N Mean_COGNITIVE_INDEX STD_COGNITIVE_INDEX Mean_A7_A5 STD_A7_A5 Mean_TMT_B_A STD_TMT_B_A Mean_relogio STD_relogio Mean_DIGIT_SPAN_BACK STD_DIGIT_SPAN_BACK Mean_Lipoxina STD_Lipoxina Mean_AB1_40 STD_AB1_40 Mean_AB1_42 STD_AB1_42 Mean_TAU STD_TAU
AD 13 -3.35 1.48 0.24 0.31 226.69 131.29 8.92 1.64 3.77 1.39 79.10 73.64 5664.22 1665.88 279.71 60.00 632.00 278.83
MCI 33 -1.48 1.28 0.54 0.30 129.73 105.03 8.61 1.84 4.70 1.60 120.24 49.46 4557.04 2559.94 413.35 306.30 444.21 196.85
CTL 77 0.21 0.65 0.82 0.18 58.53 48.00 9.29 1.21 5.84 1.74 127.15 61.52 4192.04 1915.04 533.92 242.82 354.87 194.95 
sumario_dados_hemi_v1_diag_CD <-
dados_hemi_v1_CD %>%
group_by(clinical_test, Diagnostic) %>%
summarise(
N = n_distinct(SUBJ),
Mean = mean(clinical_test_value,  na.rm = TRUE),
STD = sd(clinical_test_value,  na.rm = TRUE))

sumario_dados_hemi_v1_diag_CD %>% kable(digits = 2) %>% kable_styling()
clinical_test Diagnostic N Mean STD
A7/A5 AD 13 0.24 0.31
A7/A5 MCI 33 0.54 0.30
A7/A5 CTL 77 0.82 0.18
AB1_ratio AD 13 0.05 0.01
AB1_ratio MCI 33 0.12 0.12
AB1_ratio CTL 77 0.16 0.12
AB1-40 AD 13 5664.22 1611.83
AB1-40 MCI 33 4557.04 2522.38
AB1-40 CTL 77 4192.04 1902.56
AB1-42 AD 13 279.71 58.05
AB1-42 MCI 33 413.35 301.81
AB1-42 CTL 77 533.92 241.24
COGNITIVE_INDEX AD 13 -3.35 1.46
COGNITIVE_INDEX MCI 33 -1.48 1.27
COGNITIVE_INDEX CTL 77 0.21 0.64
DIGIT SPAN BACK AD 13 3.77 1.37
DIGIT SPAN BACK MCI 33 4.70 1.59
DIGIT SPAN BACK CTL 77 5.84 1.74
Lipoxina AD 13 79.10 71.25
Lipoxina MCI 33 120.24 48.73
Lipoxina CTL 77 127.15 61.11
TAU AD 13 632.00 269.78
TAU MCI 33 444.21 193.97
TAU CTL 77 354.87 193.68
TAU_AB1_42_ratio AD 13 2.20 0.55
TAU_AB1_42_ratio MCI 33 1.60 1.41
TAU_AB1_42_ratio CTL 77 0.79 0.60
TAU_AB1_ratio AD 13 13027.12 6874.25
TAU_AB1_ratio MCI 33 7216.44 6823.18
TAU_AB1_ratio CTL 77 3429.86 3575.63
TMT B-A AD 13 226.69 129.37
TMT B-A MCI 33 129.73 104.43
TMT B-A CTL 77 58.53 47.88 
ggplot(data = dados_hemi_v1_CD_behaviour, aes(x = Diagnostic, y = clinical_test_value, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
  geom_boxplot() +
  facet_wrap(clinical_test ~ ., scales = "free", ncol = 2) +
  stat_compare_means(method = "anova") +
  theme_pubr()

ggplot(data = dados_hemi_v1_CD_biochq, aes(x = Diagnostic, y = clinical_test_value, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
  geom_boxplot()+
  facet_wrap(clinical_test ~ ., scales = "free", ncol = 2) +
  stat_compare_means(method = "anova") +
  theme_pubr()

11.1.1 K:

11.1.2 Avg Thickness:

11.1.3 clinical data - CORRELATION AND COVARIATION

morphological_parameter clinical_test t df Correlation pvalue Diagnostic ROI Age_correction
K A7/A5 5.80 240 0.35 0.00 All Hemisphere no
K COGNITIVE_INDEX 6.70 240 0.40 0.00 All Hemisphere no
K DIGIT SPAN BACK 4.10 240 0.25 0.00 All Hemisphere no
K TMT B-A -4.80 240 -0.29 0.00 All Hemisphere no
K_age_decay A7/A5 4.30 240 0.26 0.00 All Hemisphere yes
K_age_decay COGNITIVE_INDEX 4.90 240 0.30 0.00 All Hemisphere yes
K_age_decay DIGIT SPAN BACK 3.10 240 0.19 0.00 All Hemisphere yes
K_age_decay TMT B-A -3.10 240 -0.19 0.00 All Hemisphere yes
logAvgThickness A7/A5 6.70 240 0.39 0.00 All Hemisphere no
logAvgThickness COGNITIVE_INDEX 6.80 240 0.40 0.00 All Hemisphere no
logAvgThickness DIGIT SPAN BACK 3.20 240 0.20 0.00 All Hemisphere no
logAvgThickness TMT B-A -3.50 240 -0.22 0.00 All Hemisphere no
logAvgThickness_age_decay A7/A5 4.20 240 0.26 0.00 All Hemisphere yes
logAvgThickness_age_decay COGNITIVE_INDEX 4.10 240 0.26 0.00 All Hemisphere yes
logAvgThickness_age_decay DIGIT SPAN BACK 1.80 240 0.11 0.08 All Hemisphere yes
logAvgThickness_age_decay TMT B-A -1.10 240 -0.07 0.28 All Hemisphere yes
K AB1_ratio 1.70 94 0.18 0.08 All Hemisphere no
K AB1-40 -0.76 94 -0.08 0.45 All Hemisphere no
K AB1-42 2.50 94 0.25 0.02 All Hemisphere no
K Lipoxina 0.85 92 0.09 0.40 All Hemisphere no
K TAU -2.60 94 -0.26 0.01 All Hemisphere no
K TAU_AB1_42_ratio -3.20 94 -0.31 0.00 All Hemisphere no
K TAU_AB1_ratio -2.80 94 -0.28 0.01 All Hemisphere no
K_age_decay AB1_ratio 1.60 94 0.16 0.12 All Hemisphere yes
K_age_decay AB1-40 -0.28 94 -0.03 0.78 All Hemisphere yes
K_age_decay AB1-42 2.40 94 0.24 0.02 All Hemisphere yes
K_age_decay Lipoxina 1.00 92 0.11 0.31 All Hemisphere yes
K_age_decay TAU -1.70 94 -0.17 0.09 All Hemisphere yes
K_age_decay TAU_AB1_42_ratio -2.30 94 -0.23 0.02 All Hemisphere yes
K_age_decay TAU_AB1_ratio -2.00 94 -0.20 0.05 All Hemisphere yes
logAvgThickness AB1_ratio 2.00 94 0.20 0.05 All Hemisphere no
logAvgThickness AB1-40 -2.10 94 -0.21 0.04 All Hemisphere no
logAvgThickness AB1-42 0.84 94 0.09 0.40 All Hemisphere no
logAvgThickness Lipoxina -0.51 92 -0.05 0.61 All Hemisphere no
logAvgThickness TAU -4.30 94 -0.41 0.00 All Hemisphere no
logAvgThickness TAU_AB1_42_ratio -3.50 94 -0.34 0.00 All Hemisphere no
logAvgThickness TAU_AB1_ratio -4.00 94 -0.38 0.00 All Hemisphere no
logAvgThickness_age_decay AB1_ratio 1.30 94 0.13 0.19 All Hemisphere yes
logAvgThickness_age_decay AB1-40 -1.60 94 -0.16 0.12 All Hemisphere yes
logAvgThickness_age_decay AB1-42 -0.07 94 -0.01 0.95 All Hemisphere yes
logAvgThickness_age_decay Lipoxina -0.41 92 -0.04 0.69 All Hemisphere yes
logAvgThickness_age_decay TAU -2.80 94 -0.28 0.01 All Hemisphere yes
logAvgThickness_age_decay TAU_AB1_42_ratio -1.60 94 -0.16 0.11 All Hemisphere yes
logAvgThickness_age_decay TAU_AB1_ratio -2.30 94 -0.24 0.02 All Hemisphere yes
Diagnostic morphological_parameter clinical_test t df Correlation pvalue ROI Age_correction
AD K A7/A5 0.21 24 0.04 0.83 Hemisphere no
AD K COGNITIVE_INDEX 0.70 24 0.14 0.49 Hemisphere no
AD K DIGIT SPAN BACK -0.14 24 -0.03 0.89 Hemisphere no
AD K TMT B-A -1.40 24 -0.28 0.17 Hemisphere no
AD K_age_decay A7/A5 0.58 24 0.12 0.57 Hemisphere yes
AD K_age_decay COGNITIVE_INDEX 0.94 24 0.19 0.35 Hemisphere yes
AD K_age_decay DIGIT SPAN BACK 0.19 24 0.04 0.85 Hemisphere yes
AD K_age_decay TMT B-A -1.70 24 -0.33 0.10 Hemisphere yes
AD logAvgThickness A7/A5 0.16 24 0.03 0.87 Hemisphere no
AD logAvgThickness COGNITIVE_INDEX -0.51 24 -0.10 0.61 Hemisphere no
AD logAvgThickness DIGIT SPAN BACK 1.00 24 0.21 0.31 Hemisphere no
AD logAvgThickness TMT B-A -0.09 24 -0.02 0.93 Hemisphere no
AD logAvgThickness_age_decay A7/A5 0.86 24 0.17 0.40 Hemisphere yes
AD logAvgThickness_age_decay COGNITIVE_INDEX 0.16 24 0.03 0.87 Hemisphere yes
AD logAvgThickness_age_decay DIGIT SPAN BACK 1.70 24 0.32 0.11 Hemisphere yes
AD logAvgThickness_age_decay TMT B-A -0.55 24 -0.11 0.59 Hemisphere yes
MCI K A7/A5 1.40 64 0.17 0.17 Hemisphere no
MCI K COGNITIVE_INDEX 0.88 62 0.11 0.38 Hemisphere no
MCI K DIGIT SPAN BACK 0.55 64 0.07 0.58 Hemisphere no
MCI K TMT B-A -0.01 64 0.00 0.99 Hemisphere no
MCI K_age_decay A7/A5 1.50 64 0.18 0.15 Hemisphere yes
MCI K_age_decay COGNITIVE_INDEX 0.53 62 0.07 0.60 Hemisphere yes
MCI K_age_decay DIGIT SPAN BACK 0.41 64 0.05 0.68 Hemisphere yes
MCI K_age_decay TMT B-A 0.51 64 0.06 0.61 Hemisphere yes
MCI logAvgThickness A7/A5 1.70 64 0.21 0.10 Hemisphere no
MCI logAvgThickness COGNITIVE_INDEX 0.29 62 0.04 0.77 Hemisphere no
MCI logAvgThickness DIGIT SPAN BACK -0.67 64 -0.08 0.51 Hemisphere no
MCI logAvgThickness TMT B-A 1.00 64 0.13 0.31 Hemisphere no
MCI logAvgThickness_age_decay A7/A5 1.70 64 0.20 0.10 Hemisphere yes
MCI logAvgThickness_age_decay COGNITIVE_INDEX -0.25 62 -0.03 0.80 Hemisphere yes
MCI logAvgThickness_age_decay DIGIT SPAN BACK -0.77 64 -0.10 0.45 Hemisphere yes
MCI logAvgThickness_age_decay TMT B-A 1.90 64 0.23 0.07 Hemisphere yes
CTL K A7/A5 1.10 150 0.09 0.26 Hemisphere no
CTL K COGNITIVE_INDEX 1.80 150 0.15 0.07 Hemisphere no
CTL K DIGIT SPAN BACK 1.80 150 0.14 0.08 Hemisphere no
CTL K TMT B-A -0.33 150 -0.03 0.74 Hemisphere no
CTL K_age_decay A7/A5 0.01 150 0.00 0.99 Hemisphere yes
CTL K_age_decay COGNITIVE_INDEX 1.10 150 0.09 0.26 Hemisphere yes
CTL K_age_decay DIGIT SPAN BACK 1.30 150 0.10 0.21 Hemisphere yes
CTL K_age_decay TMT B-A 0.97 150 0.08 0.33 Hemisphere yes
CTL logAvgThickness A7/A5 2.80 150 0.22 0.01 Hemisphere no
CTL logAvgThickness COGNITIVE_INDEX 4.30 150 0.33 0.00 Hemisphere no
CTL logAvgThickness DIGIT SPAN BACK 0.94 150 0.08 0.35 Hemisphere no
CTL logAvgThickness TMT B-A -0.47 150 -0.04 0.64 Hemisphere no
CTL logAvgThickness_age_decay A7/A5 1.20 150 0.10 0.24 Hemisphere yes
CTL logAvgThickness_age_decay COGNITIVE_INDEX 3.30 150 0.26 0.00 Hemisphere yes
CTL logAvgThickness_age_decay DIGIT SPAN BACK 0.23 150 0.02 0.82 Hemisphere yes
CTL logAvgThickness_age_decay TMT B-A 1.30 150 0.11 0.19 Hemisphere yes
AD K AB1_ratio -1.20 10 -0.35 0.26 Hemisphere no
AD K AB1-40 1.40 10 0.39 0.21 Hemisphere no
AD K AB1-42 1.10 10 0.34 0.28 Hemisphere no
AD K Lipoxina 0.90 10 0.27 0.39 Hemisphere no
AD K TAU 1.70 10 0.48 0.11 Hemisphere no
AD K TAU_AB1_42_ratio 1.40 10 0.40 0.20 Hemisphere no
AD K TAU_AB1_ratio 1.90 10 0.51 0.09 Hemisphere no
AD K_age_decay AB1_ratio -1.20 10 -0.35 0.27 Hemisphere yes
AD K_age_decay AB1-40 1.30 10 0.38 0.23 Hemisphere yes
AD K_age_decay AB1-42 1.10 10 0.33 0.30 Hemisphere yes
AD K_age_decay Lipoxina 1.00 10 0.31 0.32 Hemisphere yes
AD K_age_decay TAU 1.60 10 0.46 0.13 Hemisphere yes
AD K_age_decay TAU_AB1_42_ratio 1.30 10 0.38 0.23 Hemisphere yes
AD K_age_decay TAU_AB1_ratio 1.80 10 0.48 0.11 Hemisphere yes
AD logAvgThickness AB1_ratio -0.46 10 -0.14 0.66 Hemisphere no
AD logAvgThickness AB1-40 1.20 10 0.36 0.25 Hemisphere no
AD logAvgThickness AB1-42 1.40 10 0.40 0.20 Hemisphere no
AD logAvgThickness Lipoxina -0.03 10 -0.01 0.98 Hemisphere no
AD logAvgThickness TAU 1.30 10 0.37 0.23 Hemisphere no
AD logAvgThickness TAU_AB1_42_ratio 0.79 10 0.24 0.45 Hemisphere no
AD logAvgThickness TAU_AB1_ratio 1.20 10 0.36 0.24 Hemisphere no
AD logAvgThickness_age_decay AB1_ratio -0.06 10 -0.02 0.95 Hemisphere yes
AD logAvgThickness_age_decay AB1-40 0.78 10 0.24 0.45 Hemisphere yes
AD logAvgThickness_age_decay AB1-42 0.91 10 0.28 0.38 Hemisphere yes
AD logAvgThickness_age_decay Lipoxina 0.23 10 0.07 0.82 Hemisphere yes
AD logAvgThickness_age_decay TAU 0.96 10 0.29 0.36 Hemisphere yes
AD logAvgThickness_age_decay TAU_AB1_42_ratio 0.71 10 0.22 0.50 Hemisphere yes
AD logAvgThickness_age_decay TAU_AB1_ratio 0.90 10 0.27 0.39 Hemisphere yes
MCI K AB1_ratio -0.27 24 -0.05 0.79 Hemisphere no
MCI K AB1-40 2.00 24 0.37 0.06 Hemisphere no
MCI K AB1-42 2.90 24 0.51 0.01 Hemisphere no
MCI K Lipoxina 0.50 24 0.10 0.63 Hemisphere no
MCI K TAU -0.46 24 -0.09 0.65 Hemisphere no
MCI K TAU_AB1_42_ratio -1.80 24 -0.35 0.08 Hemisphere no
MCI K TAU_AB1_ratio -1.50 24 -0.30 0.14 Hemisphere no
MCI K_age_decay AB1_ratio -0.26 24 -0.05 0.79 Hemisphere yes
MCI K_age_decay AB1-40 2.20 24 0.42 0.03 Hemisphere yes
MCI K_age_decay AB1-42 3.00 24 0.53 0.01 Hemisphere yes
MCI K_age_decay Lipoxina 0.38 24 0.08 0.71 Hemisphere yes
MCI K_age_decay TAU -0.05 24 -0.01 0.96 Hemisphere yes
MCI K_age_decay TAU_AB1_42_ratio -1.40 24 -0.27 0.17 Hemisphere yes
MCI K_age_decay TAU_AB1_ratio -1.10 24 -0.22 0.29 Hemisphere yes
MCI logAvgThickness AB1_ratio 0.03 24 0.01 0.98 Hemisphere no
MCI logAvgThickness AB1-40 -0.53 24 -0.11 0.60 Hemisphere no
MCI logAvgThickness AB1-42 0.97 24 0.19 0.34 Hemisphere no
MCI logAvgThickness Lipoxina 0.77 24 0.16 0.45 Hemisphere no
MCI logAvgThickness TAU -2.50 24 -0.46 0.02 Hemisphere no
MCI logAvgThickness TAU_AB1_42_ratio -1.60 24 -0.31 0.12 Hemisphere no
MCI logAvgThickness TAU_AB1_ratio -2.20 24 -0.42 0.03 Hemisphere no
MCI logAvgThickness_age_decay AB1_ratio -0.30 24 -0.06 0.77 Hemisphere yes
MCI logAvgThickness_age_decay AB1-40 -0.17 24 -0.04 0.86 Hemisphere yes
MCI logAvgThickness_age_decay AB1-42 0.89 24 0.18 0.38 Hemisphere yes
MCI logAvgThickness_age_decay Lipoxina 0.79 24 0.16 0.44 Hemisphere yes
MCI logAvgThickness_age_decay TAU -1.50 24 -0.29 0.15 Hemisphere yes
MCI logAvgThickness_age_decay TAU_AB1_42_ratio -0.75 24 -0.15 0.46 Hemisphere yes
MCI logAvgThickness_age_decay TAU_AB1_ratio -1.30 24 -0.25 0.22 Hemisphere yes
CTL K AB1_ratio 0.66 56 0.09 0.51 Hemisphere no
CTL K AB1-40 -1.50 56 -0.20 0.14 Hemisphere no
CTL K AB1-42 -0.44 56 -0.06 0.66 Hemisphere no
CTL K Lipoxina -1.20 54 -0.17 0.22 Hemisphere no
CTL K TAU -2.00 56 -0.26 0.05 Hemisphere no
CTL K TAU_AB1_42_ratio -0.74 56 -0.10 0.46 Hemisphere no
CTL K TAU_AB1_ratio -1.00 56 -0.13 0.32 Hemisphere no
CTL K_age_decay AB1_ratio 0.77 56 0.10 0.44 Hemisphere yes
CTL K_age_decay AB1-40 -1.40 56 -0.18 0.18 Hemisphere yes
CTL K_age_decay AB1-42 -0.46 56 -0.06 0.65 Hemisphere yes
CTL K_age_decay Lipoxina -0.76 54 -0.10 0.45 Hemisphere yes
CTL K_age_decay TAU -1.30 56 -0.17 0.20 Hemisphere yes
CTL K_age_decay TAU_AB1_42_ratio -0.05 56 -0.01 0.96 Hemisphere yes
CTL K_age_decay TAU_AB1_ratio -0.36 56 -0.05 0.72 Hemisphere yes
CTL logAvgThickness AB1_ratio 0.53 56 0.07 0.60 Hemisphere no
CTL logAvgThickness AB1-40 -1.40 56 -0.18 0.18 Hemisphere no
CTL logAvgThickness AB1-42 -2.20 56 -0.28 0.03 Hemisphere no
CTL logAvgThickness Lipoxina -3.20 54 -0.40 0.00 Hemisphere no
CTL logAvgThickness TAU -2.50 56 -0.31 0.02 Hemisphere no
CTL logAvgThickness TAU_AB1_42_ratio -0.28 56 -0.04 0.78 Hemisphere no
CTL logAvgThickness TAU_AB1_ratio -0.94 56 -0.12 0.35 Hemisphere no
CTL logAvgThickness_age_decay AB1_ratio 0.51 56 0.07 0.61 Hemisphere yes
CTL logAvgThickness_age_decay AB1-40 -1.10 56 -0.14 0.28 Hemisphere yes
CTL logAvgThickness_age_decay AB1-42 -2.50 56 -0.31 0.02 Hemisphere yes
CTL logAvgThickness_age_decay Lipoxina -2.20 54 -0.29 0.03 Hemisphere yes
CTL logAvgThickness_age_decay TAU -1.40 56 -0.18 0.18 Hemisphere yes
CTL logAvgThickness_age_decay TAU_AB1_42_ratio 0.82 56 0.11 0.42 Hemisphere yes
CTL logAvgThickness_age_decay TAU_AB1_ratio 0.04 56 0.00 0.97 Hemisphere yes
morphological_parameter clinical_test t df Correlation ROI Age_correction pval.adj
K A7/A5 5.800 240 0.350 Hemisphere no 0.000
K COGNITIVE_INDEX 6.700 240 0.400 Hemisphere no 0.000
K DIGIT SPAN BACK 4.100 240 0.250 Hemisphere no 0.000
K TMT B-A -4.800 240 -0.290 Hemisphere no 0.000
K A7/A5 4.300 240 0.260 Hemisphere yes 0.000
K COGNITIVE_INDEX 4.900 240 0.300 Hemisphere yes 0.000
K DIGIT SPAN BACK 3.100 240 0.190 Hemisphere yes 0.010
K TMT B-A -3.100 240 -0.190 Hemisphere yes 0.010
logAvgThickness A7/A5 6.700 240 0.390 Hemisphere no 0.000
logAvgThickness COGNITIVE_INDEX 6.800 240 0.400 Hemisphere no 0.000
logAvgThickness DIGIT SPAN BACK 3.200 240 0.200 Hemisphere no 0.005
logAvgThickness TMT B-A -3.500 240 -0.220 Hemisphere no 0.002
logAvgThickness A7/A5 4.200 240 0.260 Hemisphere yes 0.000
logAvgThickness COGNITIVE_INDEX 4.100 240 0.260 Hemisphere yes 0.000
logAvgThickness DIGIT SPAN BACK 1.800 240 0.110 Hemisphere yes 0.307
logAvgThickness TMT B-A -1.100 240 -0.069 Hemisphere yes 1.000
K AB1_ratio 1.700 94 0.180 Hemisphere no 0.167
K AB1-40 -0.760 94 -0.078 Hemisphere no 0.894
K AB1-42 2.500 94 0.250 Hemisphere no 0.031
K Lipoxina 0.850 92 0.088 Hemisphere no 0.795
K TAU -2.600 94 -0.260 Hemisphere no 0.023
K TAU_AB1_42_ratio -3.200 94 -0.310 Hemisphere no 0.004
K TAU_AB1_ratio -2.800 94 -0.280 Hemisphere no 0.011
K AB1_ratio 1.600 94 0.160 Hemisphere yes 0.241
K AB1-40 -0.280 94 -0.029 Hemisphere yes 1.000
K AB1-42 2.400 94 0.240 Hemisphere yes 0.038
K Lipoxina 1.000 92 0.110 Hemisphere yes 0.619
K TAU -1.700 94 -0.170 Hemisphere yes 0.189
K TAU_AB1_42_ratio -2.300 94 -0.230 Hemisphere yes 0.044
K TAU_AB1_ratio -2.000 94 -0.200 Hemisphere yes 0.094
logAvgThickness AB1_ratio 2.000 94 0.200 Hemisphere no 0.104
logAvgThickness AB1-40 -2.100 94 -0.210 Hemisphere no 0.076
logAvgThickness AB1-42 0.840 94 0.086 Hemisphere no 0.805
logAvgThickness Lipoxina -0.510 92 -0.053 Hemisphere no 1.000
logAvgThickness TAU -4.300 94 -0.410 Hemisphere no 0.000
logAvgThickness TAU_AB1_42_ratio -3.500 94 -0.340 Hemisphere no 0.001
logAvgThickness TAU_AB1_ratio -4.000 94 -0.380 Hemisphere no 0.000
logAvgThickness AB1_ratio 1.300 94 0.130 Hemisphere yes 0.384
logAvgThickness AB1-40 -1.600 94 -0.160 Hemisphere yes 0.231
logAvgThickness AB1-42 -0.069 94 -0.007 Hemisphere yes 1.000
logAvgThickness Lipoxina -0.410 92 -0.042 Hemisphere yes 1.000
logAvgThickness TAU -2.800 94 -0.280 Hemisphere yes 0.011
logAvgThickness TAU_AB1_42_ratio -1.600 94 -0.160 Hemisphere yes 0.227
logAvgThickness TAU_AB1_ratio -2.300 94 -0.240 Hemisphere yes 0.042
K DIGIT SPAN BACK 2.400 240 0.160 Frontal lobe yes 0.061
K relogio -1.100 240 -0.070 Frontal lobe yes 1.000
K TMT B-A -2.000 240 -0.130 Frontal lobe yes 0.172
K DIGIT SPAN BACK 3.400 240 0.220 Frontal lobe no 0.003
K relogio 0.240 240 0.016 Frontal lobe no 1.000
K TMT B-A -3.100 240 -0.200 Frontal lobe no 0.009
logAvgThickness DIGIT SPAN BACK 2.500 240 0.160 Frontal lobe no 0.057
logAvgThickness relogio 2.200 240 0.140 Frontal lobe no 0.162
logAvgThickness TMT B-A -3.300 240 -0.210 Frontal lobe no 0.005
logAvgThickness DIGIT SPAN BACK 1.100 240 0.071 Frontal lobe yes 1.000
logAvgThickness relogio 0.950 240 0.062 Frontal lobe yes 1.000
logAvgThickness TMT B-A -1.100 240 -0.071 Frontal lobe yes 1.000
K relogio -0.390 240 -0.026 Parietal lobe yes 1.000
K relogio -0.260 240 -0.017 Parietal lobe no 1.000
logAvgThickness relogio 1.400 240 0.089 Parietal lobe no 1.000
logAvgThickness relogio -0.310 240 -0.020 Parietal lobe yes 1.000
K relogio -1.800 240 -0.120 Occipital lobe yes 0.410
K relogio -0.670 240 -0.044 Occipital lobe no 1.000
logAvgThickness relogio -1.200 240 -0.080 Occipital lobe no 1.000
logAvgThickness relogio -2.200 240 -0.140 Occipital lobe yes 0.192
K A7/A5 3.100 240 0.200 Temporal lobe yes 0.008
K A7/A5 4.900 240 0.300 Temporal lobe no 0.000
logAvgThickness A7/A5 7.500 240 0.430 Temporal lobe no 0.000
logAvgThickness A7/A5 5.700 240 0.340 Temporal lobe yes 0.000